---
_id: '54613'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In:
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly
Computing – Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85–104.
doi:10.5281/zenodo.8068466'
apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M.,
Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.),
On-The-Fly Computing – Individualized IT-services in dynamic markets (Vol.
412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023,
series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration
and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466},
booktitle={On-The-Fly Computing – Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas
Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and
Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake,
Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth,
Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga
Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration
and Evaluation.” In On-The-Fly Computing – Individualized IT-Services in Dynamic
Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco
Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe
Des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn, 2023.
https://doi.org/10.5281/zenodo.8068466.
ieee: J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly
Computing – Individualized IT-services in dynamic markets, vol. 412, C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds.
Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.
mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly
Computing – Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
85–104, doi:10.5281/zenodo.8068466.
short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A.
Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H.
Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing – Individualized IT-Services
in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.'
date_created: 2024-06-04T15:55:56Z
date_updated: 2024-06-04T15:56:45Z
department:
- _id: '574'
doi: 10.5281/zenodo.8068466
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
intvolume: ' 412'
keyword:
- dice ngonga sfb901 sherif
language:
- iso: eng
page: 85–104
publication: On-The-Fly Computing – Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Configuration and Evaluation
type: book_chapter
user_id: '67199'
volume: 412
year: '2023'
...
---
_id: '45884'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In:
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104.
doi:10.5281/zenodo.8068466'
apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M.,
Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.),
On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol.
412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023,
place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466},
booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas
Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and
Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake,
Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth,
Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: 'Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga
Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration
and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic
Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco
Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe
Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn,
2023. https://doi.org/10.5281/zenodo.8068466.'
ieee: 'J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly
Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds.
Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.'
mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
85–104, doi:10.5281/zenodo.8068466.
short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A.
Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H.
Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn,
2023, pp. 85–104.'
date_created: 2023-07-07T07:50:53Z
date_updated: 2023-07-07T11:20:12Z
ddc:
- '040'
department:
- _id: '7'
doi: 10.5281/zenodo.8068466
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
file:
- access_level: open_access
content_type: application/pdf
creator: florida
date_created: 2023-07-07T07:50:34Z
date_updated: 2023-07-07T11:20:11Z
file_id: '45885'
file_name: B2-Chapter-SFB-Buch-Final.pdf
file_size: 895091
relation: main_file
file_date_updated: 2023-07-07T11:20:11Z
has_accepted_license: '1'
intvolume: ' 412'
language:
- iso: eng
oa: '1'
page: 85-104
place: Paderborn
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Configuration and Evaluation
type: book_chapter
user_id: '477'
volume: 412
year: '2023'
...
---
_id: '30868'
abstract:
- lang: eng
text: "Algorithm configuration (AC) is concerned with the automated search of the\r\nmost
suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently
a wide variety of AC problem variants and methods proposed in the\r\nliterature.
Existing reviews do not take into account all derivatives of the AC\r\nproblem,
nor do they offer a complete classification scheme. To this end, we\r\nintroduce
taxonomies to describe the AC problem and features of configuration\r\nmethods,
respectively. We review existing AC literature within the lens of our\r\ntaxonomies,
outline relevant design choices of configuration approaches,\r\ncontrast methods
and problem variants against each other, and describe the\r\nstate of AC in industry.
Finally, our review provides researchers and\r\npractitioners with a look at future
research directions in the field of AC."
author:
- first_name: Elias
full_name: Schede, Elias
last_name: Schede
- first_name: Jasmin
full_name: Brandt, Jasmin
last_name: Brandt
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kevin
full_name: Tierney, Kevin
last_name: Tierney
citation:
ama: Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm
Configuration. arXiv:220201651. Published online 2022.
apa: Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier,
E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration.
In arXiv:2202.01651.
bibtex: '@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A
Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651},
author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel
Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022}
}'
chicago: Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever,
Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated
Algorithm Configuration.” ArXiv:2202.01651, 2022.
ieee: E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,”
arXiv:2202.01651. 2022.
mla: Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.”
ArXiv:2202.01651, 2022.
short: E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K.
Tierney, ArXiv:2202.01651 (2022).
date_created: 2022-04-12T12:00:08Z
date_updated: 2022-04-12T12:01:15Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2202.01651'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2202.01651
status: public
title: A Survey of Methods for Automated Algorithm Configuration
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '30865'
abstract:
- lang: eng
text: "The problem of selecting an algorithm that appears most suitable for a\r\nspecific
instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability
problem, is called instance-specific algorithm selection. Over\r\nthe past decade,
the problem has received considerable attention, resulting in\r\na number of different
methods for algorithm selection. Although most of these\r\nmethods are based on
machine learning, surprisingly little work has been done\r\non meta learning,
that is, on taking advantage of the complementarity of\r\nexisting algorithm selection
methods in order to combine them into a single\r\nsuperior algorithm selector.
In this paper, we introduce the problem of meta\r\nalgorithm selection, which
essentially asks for the best way to combine a given\r\nset of algorithm selectors.
We present a general methodological framework for\r\nmeta algorithm selection
as well as several concrete learning methods as\r\ninstantiations of this framework,
essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive
experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors
can significantly outperform single\r\nalgorithm selectors and have the potential
to form the new state of the art in\r\nalgorithm selection."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Lukas
full_name: Gehring, Lukas
last_name: Gehring
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection
on a Meta Level. Machine Learning. Published online 2022.
apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E.
(2022). Algorithm Selection on a Meta Level. In Machine Learning.
bibtex: '@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm
Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander
and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2022} }'
chicago: Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever,
and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning,
2022.
ieee: A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm
Selection on a Meta Level,” Machine Learning. 2022.
mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine
Learning, 2022.
short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning
(2022).
date_created: 2022-04-12T11:55:18Z
date_updated: 2022-08-24T12:45:39Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2107.09414'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Machine Learning
status: public
title: Algorithm Selection on a Meta Level
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '33090'
abstract:
- lang: eng
text: 'AbstractHeated tool butt welding is a method
often used for joining thermoplastics, especially when the components are made
out of different materials. The quality of the connection between the components
crucially depends on a suitable choice of the parameters of the welding process,
such as heating time, temperature, and the precise way how the parts are then
welded. Moreover, when different materials are to be joined, the parameter values
need to be tailored to the specifics of the respective material. To this end,
in this paper, three approaches to tailor the parameter values to optimize the
quality of the connection are compared: a heuristic by Potente, statistical experimental
design, and Bayesian optimization. With the suitability for practice in mind,
a series of experiments are carried out with these approaches, and their capabilities
of proposing well-performing parameter values are investigated. As a result, Bayesian
optimization is found to yield peak performance, but the costs for optimization
are substantial. In contrast, the Potente heuristic does not require any experimentation
and recommends parameter values with competitive quality.'
author:
- first_name: Karina
full_name: Gevers, Karina
id: '83151'
last_name: Gevers
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Volker
full_name: Schöppner, Volker
id: '20530'
last_name: Schöppner
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of
heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials. Welding in the World. Published online 2022.
doi:10.1007/s40194-022-01339-9
apa: Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E.
(2022). A comparison of heuristic, statistical, and machine learning methods for
heated tool butt welding of two different materials. Welding in the World.
https://doi.org/10.1007/s40194-022-01339-9
bibtex: '@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison
of heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials}, DOI={10.1007/s40194-022-01339-9},
journal={Welding in the World}, publisher={Springer Science and Business Media
LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik
and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }'
chicago: Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner,
and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning
Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in
the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.
ieee: 'K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A
comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials,” Welding in the World, 2022,
doi: 10.1007/s40194-022-01339-9.'
mla: Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine
Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding
in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.
short: K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding
in the World (2022).
date_created: 2022-08-24T12:51:07Z
date_updated: 2022-08-24T12:52:06Z
doi: 10.1007/s40194-022-01339-9
keyword:
- Metals and Alloys
- Mechanical Engineering
- Mechanics of Materials
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Welding in the World
publication_identifier:
issn:
- 0043-2288
- 1878-6669
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: A comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials
type: journal_article
user_id: '38209'
year: '2022'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21092'
abstract:
- lang: eng
text: "Automated Machine Learning (AutoML) seeks to automatically find so-called
machine learning pipelines that maximize the prediction performance when being
used to train a model on a given dataset. One of the main and yet open challenges
in AutoML is an effective use of computational resources: An AutoML process involves
the evaluation of many candidate pipelines, which are costly but often ineffective
because they are canceled due to a timeout.\r\nIn this paper, we present an approach
to predict the runtime of two-step machine learning pipelines with up to one pre-processor,
which can be used to anticipate whether or not a pipeline will time out. Separate
runtime models are trained offline for each algorithm that may be used in a pipeline,
and an overall prediction is derived from these models. We empirically show that
the approach increases successful evaluations made by an AutoML tool while preserving
or even improving on the previously best solutions."
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline
Runtimes in the Context of Automated Machine Learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting
Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning
Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE
Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE},
author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier,
Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
n.d.
ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine
Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE
Transactions on Pattern Analysis and Machine Intelligence.
mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context
of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, IEEE.
short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (n.d.).
date_created: 2021-01-27T13:45:52Z
date_updated: 2022-01-06T06:54:45Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: accepted
publisher: IEEE
status: public
title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '22913'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded
Rationality, and Rational Metareasoning. In: ; 2021.'
apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated
Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD
Workshop on Automating Data Science, Bilbao (Virtual).
bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine
Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier,
Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021}
}'
chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever.
“Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,”
2021.
ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning,
Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop
on Automating Data Science, Bilbao (Virtual), 2021.
mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality,
and Rational Metareasoning. 2021.
short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.'
conference:
end_date: 2021-09-17
location: Bilbao (Virtual)
name: ECML/PKDD Workshop on Automating Data Science
start_date: 2021-09-13
date_created: 2021-08-02T07:46:29Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
quality_controlled: '1'
status: public
title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '22914'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML.
In: ; 2021.'
apa: Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML
by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.
bibtex: '@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in
AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021}
}'
chicago: Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in
AutoML by Naive AutoML,” 2021.
ieee: F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive
AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual,
2021.
mla: Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in
AutoML by Naive AutoML. 2021.
short: 'F. Mohr, M.D. Wever, in: 2021.'
conference:
end_date: 2021-07-23
location: Virtual
name: 8th ICML Workshop on Automated Machine Learning
start_date: 2021-07-23
date_created: 2021-08-02T07:48:07Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
status: public
title: Replacing the Ex-Def Baseline in AutoML by Naive AutoML
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '30866'
abstract:
- lang: eng
text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof
machine learning algorithms and their composition into an overall (software)\r\nsolution
- a machine learning pipeline - tailored to the learning task\r\n(dataset) at
hand. Over the last decade, AutoML has developed into an\r\nindependent research
field with hundreds of contributions. While AutoML offers\r\nmany prospects, it
is also known to be quite resource-intensive, which is one\r\nof its major points
of criticism. The primary cause for a high resource\r\nconsumption is that many
approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines
while searching for good candidates. This problem is\r\namplified in the context
of research on AutoML methods, due to large scale\r\nexperiments conducted with
many datasets and approaches, each of them being run\r\nwith several repetitions
to rule out random effects. In the spirit of recent\r\nwork on Green AI, this
paper is written in an attempt to raise the awareness of\r\nAutoML researchers
for the problem and to elaborate on possible remedies. To\r\nthis end, we identify
four categories of actions the community may take towards\r\nmore sustainable
research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking,
transparency and research incentives."
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards
Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850.
Published online 2021.'
apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier,
E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
In arXiv:2111.05850.'
bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards
Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850},
author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik
Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning:
Status Quo and Future Directions.” ArXiv:2111.05850, 2021.'
ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier,
“Towards Green Automated Machine Learning: Status Quo and Future Directions,”
arXiv:2111.05850. 2021.'
mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo
and Future Directions.” ArXiv:2111.05850, 2021.'
short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier,
ArXiv:2111.05850 (2021).
date_created: 2022-04-12T11:57:15Z
date_updated: 2022-04-12T12:01:23Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2111.05850'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2111.05850
status: public
title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions'
type: preprint
user_id: '38209'
year: '2021'
...
---
_id: '27284'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: Wever MD. Automated Machine Learning for Multi-Label Classification.;
2021. doi:10.17619/UNIPB/1-1302
apa: Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification.
https://doi.org/10.17619/UNIPB/1-1302
bibtex: '@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification},
DOI={10.17619/UNIPB/1-1302},
author={Wever, Marcel Dominik}, year={2021} }'
chicago: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification,
2021. https://doi.org/10.17619/UNIPB/1-1302.
ieee: M. D. Wever, Automated Machine Learning for Multi-Label Classification.
2021.
mla: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification.
2021, doi:10.17619/UNIPB/1-1302.
short: M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.
date_created: 2021-11-08T14:05:19Z
date_updated: 2022-04-13T09:39:56Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.17619/UNIPB/1-1302
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2022-04-13T09:35:25Z
date_updated: 2022-04-13T09:39:56Z
file_id: '30886'
file_name: dissertation_publish_upload.pdf
file_size: 8098177
relation: main_file
file_date_updated: 2022-04-13T09:39:56Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication_status: published
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Automated Machine Learning for Multi-Label Classification
type: dissertation
user_id: '33176'
year: '2021'
...
---
_id: '21198'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset
Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published
online 2021.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021).
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD-2021), Delhi, India.'
bibtex: '@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm
Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise
Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm
Selectors from Imprecise Performance Data.” PAKDD, 2021.'
ieee: 'J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection
as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance
Data.” 2021.'
mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning:
Constructing Algorithm Selectors from Imprecise Performance Data. 2021.'
short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).
conference:
end_date: 2021-05-14
location: Delhi, India
name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
start_date: 2021-05-11
date_created: 2021-02-09T09:30:14Z
date_updated: 2022-08-24T12:49:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
series_title: PAKDD
status: public
title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data'
type: conference
user_id: '38209'
year: '2021'
...
---
_id: '17407'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic
Feature Representation. In: Discovery Science. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm
Selection with Dyadic Feature Representation. Discovery Science. Discovery
Science 2020.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm
Selection with Dyadic Feature Representation}, booktitle={Discovery Science},
author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020}
}'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme
Algorithm Selection with Dyadic Feature Representation.” In Discovery Science,
2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection
with Dyadic Feature Representation,” presented at the Discovery Science 2020,
2020.
mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature
Representation.” Discovery Science, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.'
conference:
name: Discovery Science 2020
date_created: 2020-07-21T10:06:51Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Discovery Science
status: public
title: Extreme Algorithm Selection with Dyadic Feature Representation
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17408'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression
for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence.
; 2020.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020).
Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in
Artificial Intelligence. 43rd German Conference on Artificial Intelligence.'
bibtex: '@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking
and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial
Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI
2020: Advances in Artificial Intelligence, 2020.'
ieee: J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking
and Regression for Algorithm Selection,” presented at the 43rd German Conference
on Artificial Intelligence, 2020.
mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm
Selection.” KI 2020: Advances in Artificial Intelligence, 2020.'
short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances
in Artificial Intelligence, 2020.'
conference:
name: 43rd German Conference on Artificial Intelligence
date_created: 2020-07-21T10:21:09Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: 'KI 2020: Advances in Artificial Intelligence'
status: public
title: Hybrid Ranking and Regression for Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17424'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive
Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020.
; 2020. doi:10.1007/978-3-030-66770-2_8'
apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020).
AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of
the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8'
bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML
for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8},
booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede,
Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020}
}'
chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and
Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”
In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.'
ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML
for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream
Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.'
mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL
Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.'
short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings
of the ECMLPKDD 2020, 2020.'
conference:
name: IOTStream Workshop @ ECMLPKDD 2020
date_created: 2020-07-28T09:17:41Z
date_updated: 2022-01-06T06:53:11Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1007/978-3-030-66770-2_8
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '1'
name: SFB 901
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the ECMLPKDD 2020
status: public
title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17605'
abstract:
- lang: eng
text: "Syntactic annotation of corpora in the form of part-of-speech (POS) tags
is a key requirement for both linguistic research and subsequent automated natural
language processing (NLP) tasks. This problem is commonly tackled using machine
learning methods, i.e., by training a POS tagger on a sufficiently large corpus
of labeled data. \r\nWhile the problem of POS tagging can essentially be considered
as solved for modern languages, historical corpora turn out to be much more difficult,
especially due to the lack of native speakers and sparsity of training data. Moreover,
most texts have no sentences as we know them today, nor a common orthography.\r\nThese
irregularities render the task of automated POS tagging more difficult and error-prone.
Under these circumstances, instead of forcing the POS tagger to predict and commit
to a single tag, it should be enabled to express its uncertainty. In this paper,
we consider POS tagging within the framework of set-valued prediction, which allows
the POS tagger to express its uncertainty via predicting a set of candidate POS
tags instead of guessing a single one. The goal is to guarantee a high confidence
that the correct POS tag is included while keeping the number of candidates small.\r\nIn
our experimental study, we find that extending state-of-the-art POS taggers to
set-valued prediction yields more precise and robust taggings, especially for
unknown words, i.e., words not occurring in the training data."
author:
- first_name: Stefan Helmut
full_name: Heid, Stefan Helmut
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical
Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
apa: Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech
Tagging of Historical Corpora through Set-Valued Prediction. In Journal of
Data Mining and Digital Humanities. episciences.
bibtex: '@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging
of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data
Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan
Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }'
chicago: Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable
Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal
of Data Mining and Digital Humanities. episciences, n.d.
ieee: S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging
of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining
and Digital Humanities. episciences.
mla: Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical
Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital
Humanities, episciences.
short: S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital
Humanities (n.d.).
date_created: 2020-08-05T06:52:53Z
date_updated: 2022-01-06T06:53:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2008.01377
oa: '1'
project:
- _id: '39'
name: InterGramm
publication: Journal of Data Mining and Digital Humanities
publication_status: submitted
publisher: episciences
status: public
title: Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
type: preprint
user_id: '5786'
year: '2020'
...
---
_id: '20306'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In:
Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm
Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020
@ NeurIPS 2020, Online.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm
Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede,
Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards
Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,”
presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.
mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop
MetaLearn 2020 @ NeurIPS 2020, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS
2020, 2020.'
conference:
location: Online
name: Workshop MetaLearn 2020 @ NeurIPS 2020
date_created: 2020-11-06T09:42:27Z
date_updated: 2022-01-06T06:54:26Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Workshop MetaLearn 2020 @ NeurIPS 2020
status: public
title: Towards Meta-Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '18276'
abstract:
- lang: eng
text: "Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom
a fixed set of candidate algorithms most suitable for a specific instance\r\nof
an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's
runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training
data for algorithm selection models is usually generated\r\nunder time constraints
in the sense that not all algorithms are run to\r\ncompletion on all instances.
Thus, training data usually comprises censored\r\ninformation, as the true runtime
of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches
are not able to handle such information in\r\na proper way. On the other side,
survival analysis (SA) naturally supports\r\ncensored data and offers appropriate
ways to use such data for learning\r\ndistributional models of algorithm runtime,
as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated
decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive.
Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse
approach to algorithm\r\nselection, in which the avoidance of a timeout is given
high priority. In an\r\nextensive experimental study with the standard benchmark
ASlib, our approach is\r\nshown to be highly competitive and in many cases even
superior to\r\nstate-of-the-art AS approaches."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic
Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020.
; 2020.'
apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020).
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival
Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok,
Thailand.'
bibtex: '@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis},
booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and
Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and
Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection
Based on Survival Analysis.” In ACML 2020, 2020.'
ieee: 'A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,”
presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand,
2020.'
mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to
Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.'
short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020,
2020.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: 12th Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-08-25T12:09:28Z
date_updated: 2022-01-06T06:53:28Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- url: https://arxiv.org/pdf/2007.02816.pdf
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ACML 2020
status: public
title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on
Survival Analysis'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '15629'
abstract:
- lang: eng
text: In multi-label classification (MLC), each instance is associated with a set
of class labels, in contrast to standard classification where an instance is assigned
a single label. Binary relevance (BR) learning, which reduces a multi-label to
a set of binary classification problems, one per label, is arguably the most straight-forward
approach to MLC. In spite of its simplicity, BR proved to be competitive to more
sophisticated MLC methods, and still achieves state-of-the-art performance for
many loss functions. Somewhat surprisingly, the optimal choice of the base learner
for tackling the binary classification problems has received very little attention
so far. Taking advantage of the label independence assumption inherent to BR,
we propose a label-wise base learner selection method optimizing label-wise macro
averaged performance measures. In an extensive experimental evaluation, we find
that or approach, called LiBRe, can significantly improve generalization performance.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of
Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe:
Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.
Symposium on Intelligent Data Analysis, Konstanz, Germany.'
bibtex: '@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification},
publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and
Mohr, Felix and Hüllermeier, Eyke} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification.” Springer, n.d.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification,”
presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.'
mla: 'Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners
in Binary Relevance for Multi-Label Classification. Springer.'
short: 'M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.'
conference:
end_date: 2020-04-27
location: Konstanz, Germany
name: Symposium on Intelligent Data Analysis
start_date: 2020-04-24
date_created: 2020-01-23T08:44:08Z
date_updated: 2022-01-06T06:52:30Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication_status: accepted
publisher: Springer
status: public
title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification'
type: conference
user_id: '5786'
year: '2020'
...