---
_id: '10232'
abstract:
- lang: eng
text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn,
and more recently ML-Plan, have shown impressive results for the tasks of single-label
classification and regression. Yet, there is only little work on other types of
machine learning problems so far. In particular, there is almost no work on automating
the engineering of machine learning solutions for multi-label classification (MLC).
We show how the scope of ML-Plan, an AutoML-tool for multi-class classification,
can be extended towards MLC using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, nesting other multi-label classifiers for meta algorithms
and single-label classifiers provided by WEKA as base learners. In our evaluation,
we find that the proposed approach yields strong results and performs significantly
better than a set of baselines we compare with.
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: Felix
full_name: Mohr, Felix
last_name: Mohr
- 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: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification
Extending ML-Plan. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating
Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop
on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label
Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix
and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier.
“Automating Multi-Label Classification Extending ML-Plan,” 2019.
ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label
Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated
Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending
ML-Plan. 2019.
short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.'
conference:
end_date: 2019-06-15
location: Long Beach, CA, USA
name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)
start_date: 2019-06-09
date_created: 2019-06-11T21:33:06Z
date_updated: 2022-01-06T06:50:33Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2019-09-10T08:19:01Z
date_updated: 2019-09-10T08:20:44Z
file_id: '13177'
file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
file_size: 388191
relation: main_file
file_date_updated: 2019-09-10T08:20:44Z
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
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Automating Multi-Label Classification Extending ML-Plan
type: conference
user_id: '33176'
year: '2019'
...
---
_id: '20243'
author:
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Giuseppe
full_name: Leonardi, Giuseppe
last_name: Leonardi
- first_name: Iris
full_name: Nomikou, Iris
last_name: Nomikou
- first_name: Joanna
full_name: Rączaszek-Leonardi, Joanna
last_name: Rączaszek-Leonardi
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal
Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE
Transactions on Cognitive and Developmental Systems. Published online 2019.
doi:10.1109/TCDS.2019.2892991'
apa: 'Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier,
E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and
Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems.
https://doi.org/10.1109/TCDS.2019.2892991'
bibtex: '@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019,
title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches}, DOI={10.1109/TCDS.2019.2892991},
journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing,
Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna
and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi,
and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges,
and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems,
2019. https://doi.org/10.1109/TCDS.2019.2892991.'
ieee: 'K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier,
“Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,”
IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.'
mla: 'Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological
Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental
Systems, 2019, doi:10.1109/TCDS.2019.2892991.'
short: K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier,
IEEE Transactions on Cognitive and Developmental Systems (2019).
date_created: 2020-11-02T13:25:49Z
date_updated: 2023-02-01T12:39:19Z
department:
- _id: '749'
- _id: '355'
doi: 10.1109/TCDS.2019.2892991
language:
- iso: eng
publication: IEEE Transactions on Cognitive and Developmental Systems
status: public
title: 'Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches'
type: journal_article
user_id: '14931'
year: '2019'
...
---
_id: '2479'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Amin
full_name: Faez, Amin
last_name: Faez
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition
of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018.
doi:10.1109/SCC.2018.00039'
apa: 'Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards
the Automated Composition of Machine Learning Services. In SCC. San Francisco,
CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco,
CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services},
DOI={10.1109/SCC.2018.00039},
booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik
and Hüllermeier, Eyke and Faez, Amin}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP)
Towards the Automated Composition of Machine Learning Services.” In SCC.
San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.'
ieee: F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated
Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA,
2018.
mla: Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning
Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco,
CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-24T08:34:52Z
date_updated: 2022-01-06T06:56:35Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00039
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:08:39Z
date_updated: 2018-11-06T15:08:39Z
file_id: '5382'
file_name: 08456425.pdf
file_size: 237890
relation: main_file
file_date_updated: 2018-11-06T15:08:39Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/document/8456425
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publication_status: published
publisher: IEEE
status: public
title: (WIP) Towards the Automated Composition of Machine Learning Services
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '2857'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- 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, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning.
In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI;
2018:31-39.'
apa: 'Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic
Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning (pp. 31–39). Delft, Netherlands: AAAI.'
bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic
Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier,
Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }'
chicago: Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever.
“Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop
on Hierarchical Planning, 31–39. AAAI, 2018.
ieee: F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task
Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning, Delft, Netherlands, 2018, pp. 31–39.
mla: Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of
the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
short: 'F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the
1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.'
conference:
end_date: 2018-06-29
location: Delft, Netherlands
name: 28th International Conference on Automated Planning and Scheduling
start_date: 2018-06-24
date_created: 2018-05-24T09:00:20Z
date_updated: 2022-01-06T06:58:08Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:18:26Z
date_updated: 2018-11-06T15:18:26Z
file_id: '5384'
file_name: Mohr18ProgrammaticPlanning.pdf
file_size: 349958
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:18:26Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf
oa: '1'
page: 31-39
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning
publisher: AAAI
status: public
title: Programmatic Task Network Planning
type: conference
user_id: '315'
year: '2018'
...
---
_id: '2471'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes.
In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction
with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society.
https://doi.org/10.1109/SCC.2018.00036'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA},
title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036},
booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service
Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer
Society, 2018. https://doi.org/10.1109/SCC.2018.00036.'
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction
with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
mla: Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC,
IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San
Francisco, CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-23T11:40:20Z
date_updated: 2022-01-06T06:56:32Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00036
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:15:38Z
date_updated: 2018-11-06T15:15:38Z
file_id: '5383'
file_name: 08456422.pdf
file_size: 356132
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:15:38Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/abstract/document/8456422
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publisher: IEEE Computer Society
status: public
title: On-The-Fly Service Construction with Prototypes
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3402'
abstract:
- lang: eng
text: In machine learning, so-called nested dichotomies are utilized as a reduction
technique, i.e., to decompose a multi-class classification problem into a set
of binary problems, which are solved using a simple binary classifier as a base
learner. The performance of the (multi-class) classifier thus produced strongly
depends on the structure of the decomposition. In this paper, we conduct an empirical
study, in which we compare existing heuristics for selecting a suitable structure
in the form of a nested dichotomy. Moreover, we propose two additional heuristics
as natural completions. One of them is the Best-of-K heuristic, which picks the
(presumably) best among K randomly generated nested dichotomies. Surprisingly,
and in spite of its simplicity, it turns out to outperform the state of the art.
author:
- first_name: Vitalik
full_name: Melnikov, Vitalik
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning
nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1'
apa: 'Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics
for learning nested dichotomies: an empirical analysis. Machine Learning.
https://doi.org/10.1007/s10994-018-5733-1'
bibtex: '@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics
for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1},
journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke},
year={2018} }'
chicago: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics
for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning,
2018. https://doi.org/10.1007/s10994-018-5733-1.'
ieee: 'V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning
nested dichotomies: an empirical analysis,” Machine Learning, 2018.'
mla: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics
for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning,
2018, doi:10.1007/s10994-018-5733-1.'
short: V. Melnikov, E. Hüllermeier, Machine Learning (2018).
date_created: 2018-06-29T07:44:26Z
date_updated: 2022-01-06T06:59:14Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/s10994-018-5733-1
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:30:57Z
date_updated: 2018-11-02T15:30:57Z
file_id: '5305'
file_name: OnTheEffectivenessOfHeuristics.pdf
file_size: 1482882
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:30:57Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Machine Learning
publication_identifier:
issn:
- 1573-0565
status: public
title: 'On the effectiveness of heuristics for learning nested dichotomies: an empirical
analysis'
type: journal_article
user_id: '15504'
year: '2018'
...
---
_id: '3510'
abstract:
- lang: eng
text: Automated machine learning (AutoML) seeks to automatically select, compose,
and parametrize machine learning algorithms, so as to achieve optimal performance
on a given task (dataset). Although current approaches to AutoML have already
produced impressive results, the field is still far from mature, and new techniques
are still being developed. In this paper, we present ML-Plan, a new approach to
AutoML based on hierarchical planning. To highlight the potential of this approach,
we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn,
and TPOT. In an extensive series of experiments, we show that ML-Plan is highly
competitive and often outperforms existing approaches.
article_type: original
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine
Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z'
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z},
journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515.
https://doi.org/10.1007/s10994-018-5735-z.'
ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi:
10.1007/s10994-018-5735-z.'
mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.'
short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
end_date: 2018-09-14
location: Dublin, Ireland
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:32:16Z
date_updated: 2018-11-02T15:32:16Z
file_id: '5306'
file_name: ML-PlanAutomatedMachineLearnin.pdf
file_size: 1070937
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
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: Machine Learning
publication_identifier:
eissn:
- 1573-0565
issn:
- 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...
---
_id: '3552'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification.
In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch,
the Netherlands. doi:10.1007/978-3-030-01768-2_19'
apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for
Multi-Class Classification. In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands},
title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19},
booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr,
Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps
for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19.
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class
Classification,” in Proceedings of the Symposium on Intelligent Data Analysis,
‘s-Hertogenbosch, the Netherlands.
mla: Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings
of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on
Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.'
conference:
end_date: 2018-10-26
location: ‘s-Hertogenbosch, the Netherlands
name: Symposium on Intelligent Data Analysis
start_date: 2018-10-24
date_created: 2018-07-13T15:29:15Z
date_updated: 2022-01-06T06:59:25Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/978-3-030-01768-2_19
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:23:02Z
date_updated: 2018-11-06T15:23:02Z
file_id: '5385'
file_name: Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf
file_size: 1348768
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:23:02Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19
oa: '1'
place: ‘s-Hertogenbosch, the Netherlands
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the Symposium on Intelligent Data Analysis
publication_status: accepted
quality_controlled: '1'
status: public
title: Reduction Stumps for Multi-Class Classification
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3852'
abstract:
- lang: eng
text: "In automated machine learning (AutoML), the process of engineering machine
learning applications with respect to a specific problem is (partially) automated.\r\nVarious
AutoML tools have already been introduced to provide out-of-the-box machine learning
functionality.\r\nMore specifically, by selecting machine learning algorithms
and optimizing their hyperparameters, these tools produce a machine learning pipeline
tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
follows an evolutionary approach, it suffers from performance issues when dealing
with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
a hierarchical planning to configure machine learning pipelines that are unlimited
in length.\r\nWe evaluate our approach and find its performance to be competitive
with other AutoML tools, including TPOT."
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: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...
---
_id: '2109'
abstract:
- lang: eng
text: In multinomial classification, reduction techniques are commonly used to decompose
the original learning problem into several simpler problems. For example, by recursively
bisecting the original set of classes, so-called nested dichotomies define a set
of binary classification problems that are organized in the structure of a binary
tree. In contrast to the existing one-shot heuristics for constructing nested
dichotomies and motivated by recent work on algorithm configuration, we propose
a genetic algorithm for optimizing the structure of such dichotomies. A key component
of this approach is the proposed genetic representation that facilitates the application
of standard genetic operators, while still supporting the exchange of partial
solutions under recombination. We evaluate the approach in an extensive experimental
study, showing that it yields classifiers with superior 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: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for
Classification. In: Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM;
2018. doi:10.1145/3205455.3205562'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved
Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto,
Japan: ACM. https://doi.org/10.1145/3205455.3205562'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles
of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel
Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of
Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19,
2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.'
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies
for Classification,” in Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018.
mla: Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for
Classification.” Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018,
ACM, Kyoto, Japan, 2018.'
conference:
end_date: 2018-07-19
location: Kyoto, Japan
name: GECCO 2018
start_date: 2018-07-15
date_created: 2018-03-31T13:51:23Z
date_updated: 2022-01-06T06:54:45Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1145/3205455.3205562
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:33:54Z
date_updated: 2018-11-02T14:33:54Z
file_id: '5275'
file_name: p561-wever.pdf
file_size: 875404
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:33:54Z
has_accepted_license: '1'
keyword:
- Classification
- Hierarchical Decomposition
- Indirect Encoding
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?doid=3205455.3205562
oa: '1'
place: Kyoto, Japan
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, GECCO
2018, Kyoto, Japan, July 15-19, 2018
publication_status: published
publisher: ACM
status: public
title: Ensembles of Evolved Nested Dichotomies for Classification
type: conference
user_id: '33176'
year: '2018'
...
---
_id: '17713'
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: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based
on ML-Plan. Published online 2018.
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label
Classification based on ML-Plan. Arxiv.
bibtex: '@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification
based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr,
Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label
Classification Based on ML-Plan.” Arxiv, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification
based on ML-Plan.” Arxiv, 2018.
mla: Wever, Marcel Dominik, et al. Automated Multi-Label Classification Based
on ML-Plan. Arxiv, 2018.
short: M.D. Wever, F. Mohr, E. Hüllermeier, (2018).
date_created: 2020-08-07T11:38:10Z
date_updated: 2022-01-06T06:53:17Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1811.04060.pdf
oa: '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
publisher: Arxiv
status: public
title: Automated Multi-Label Classification based on ML-Plan
type: preprint
user_id: '5786'
year: '2018'
...
---
_id: '17714'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition.
Published online 2018.
apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine
learning service composition.
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={Automated machine learning
service composition}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2018} }'
chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine
Learning Service Composition,” 2018.
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service
composition.” 2018.
mla: Mohr, Felix, et al. Automated Machine Learning Service Composition.
2018.
short: F. Mohr, M.D. Wever, E. Hüllermeier, (2018).
date_created: 2020-08-07T11:40:13Z
date_updated: 2022-01-06T06:53:17Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1809.00486.pdf
oa: '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
status: public
title: Automated machine learning service composition
type: preprint
user_id: '5786'
year: '2018'
...
---
_id: '6423'
author:
- first_name: Dirk
full_name: Schäfer, Dirk
last_name: Schäfer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad
Ranking. In: Discovery Science. Cham: Springer International Publishing;
2018:161-175. doi:10.1007/978-3-030-01771-2_11'
apa: 'Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement
Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham:
Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11'
bibtex: '@inbook{Schäfer_Hüllermeier_2018, place={Cham}, title={Preference-Based
Reinforcement Learning Using Dyad Ranking}, DOI={10.1007/978-3-030-01771-2_11},
booktitle={Discovery Science}, publisher={Springer International Publishing},
author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={161–175} }'
chicago: 'Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning
Using Dyad Ranking.” In Discovery Science, 161–75. Cham: Springer International
Publishing, 2018. https://doi.org/10.1007/978-3-030-01771-2_11.'
ieee: 'D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using
Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing,
2018, pp. 161–175.'
mla: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning
Using Dyad Ranking.” Discovery Science, Springer International Publishing,
2018, pp. 161–75, doi:10.1007/978-3-030-01771-2_11.
short: 'D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International
Publishing, Cham, 2018, pp. 161–175.'
date_created: 2018-12-20T15:52:03Z
date_updated: 2022-01-06T07:03:04Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/978-3-030-01771-2_11
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2019-01-11T11:03:50Z
date_updated: 2019-01-11T11:03:50Z
file_id: '6623'
file_name: Schäfer-Hüllermeier2018_Chapter_Preference-BasedReinforcementL.pdf
file_size: 458972
relation: main_file
success: 1
file_date_updated: 2019-01-11T11:03:50Z
has_accepted_license: '1'
language:
- iso: eng
page: 161-175
place: Cham
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Discovery Science
publication_identifier:
isbn:
- '9783030017705'
- '9783030017712'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: Preference-Based Reinforcement Learning Using Dyad Ranking
type: book_chapter
user_id: '49109'
year: '2018'
...
---
_id: '10591'
alternative_title:
- Manifesto from Dagstuhl Perspectives Workshop 16151
citation:
ama: Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles
of Data Management. Vol 7.; 2018:1-29.
apa: Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David,
C., … Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management
(Vol. 7, pp. 1–29).
bibtex: '@book{Abiteboul_Arenas_Barceló_Bienvenu_Calvanese_David_Hull_Hüllermeier_Kimelfeld_Libkin_et
al._2018, title={Research Directions for Principles of Data Management}, volume={7},
number={1}, year={2018}, pages={1–29} }'
chicago: Abiteboul, S., M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David,
R. Hull, et al., eds. Research Directions for Principles of Data Management.
Vol. 7, 2018.
ieee: S. Abiteboul et al., Eds., Research Directions for Principles of
Data Management, vol. 7, no. 1. 2018, pp. 1–29.
mla: Abiteboul, S., et al., editors. Research Directions for Principles of Data
Management. Vol. 7, no. 1, 2018, pp. 1–29.
short: S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David,
R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak,
F. Neven, M. Ortiz, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu,
K. Yi, eds., Research Directions for Principles of Data Management, 2018.
date_created: 2019-07-09T15:58:12Z
date_updated: 2022-01-06T06:50:45Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
editor:
- first_name: S.
full_name: Abiteboul, S.
last_name: Abiteboul
- first_name: M.
full_name: Arenas, M.
last_name: Arenas
- first_name: P.
full_name: Barceló, P.
last_name: Barceló
- first_name: M.
full_name: Bienvenu, M.
last_name: Bienvenu
- first_name: D.
full_name: Calvanese, D.
last_name: Calvanese
- first_name: C.
full_name: David, C.
last_name: David
- first_name: R.
full_name: Hull, R.
last_name: Hull
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: B.
full_name: Kimelfeld, B.
last_name: Kimelfeld
- first_name: L.
full_name: Libkin, L.
last_name: Libkin
- first_name: W.
full_name: Martens, W.
last_name: Martens
- first_name: T.
full_name: Milo, T.
last_name: Milo
- first_name: F.
full_name: Murlak, F.
last_name: Murlak
- first_name: F.
full_name: Neven, F.
last_name: Neven
- first_name: M.
full_name: Ortiz, M.
last_name: Ortiz
- first_name: T.
full_name: Schwentick, T.
last_name: Schwentick
- first_name: J.
full_name: Stoyanovich, J.
last_name: Stoyanovich
- first_name: J.
full_name: Su, J.
last_name: Su
- first_name: D.
full_name: Suciu, D.
last_name: Suciu
- first_name: V.
full_name: Vianu, V.
last_name: Vianu
- first_name: K.
full_name: Yi, K.
last_name: Yi
intvolume: ' 7'
issue: '1'
language:
- iso: eng
page: 1-29
status: public
title: Research Directions for Principles of Data Management
type: conference_editor
user_id: '49109'
volume: 7
year: '2018'
...
---
_id: '10783'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data:
A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A,
Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.'
apa: 'Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete
Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim,
A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence
(pp. 31–46). Springer.'
bibtex: '@inbook{Couso_Hüllermeier_2018, title={Statistical Inference for Incomplete
Ranking Data: A Comparison of two likelihood-based estimators}, booktitle={Frontiers
in Computational Intelligence}, publisher={Springer}, author={Couso, Ines and
Hüllermeier, Eyke}, editor={Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt,
ChristianEditors}, year={2018}, pages={31–46} }'
chicago: 'Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete
Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Frontiers
in Computational Intelligence, edited by Sanaz Mostaghim, Andreas Nürnberger,
and Christian Borgelt, 31–46. Springer, 2018.'
ieee: 'I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking
Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational
Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer,
2018, pp. 31–46.'
mla: 'Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking
Data: A Comparison of Two Likelihood-Based Estimators.” Frontiers in Computational
Intelligence, edited by Sanaz Mostaghim et al., Springer, 2018, pp. 31–46.'
short: 'I. Couso, E. Hüllermeier, in: S. Mostaghim, A. Nürnberger, C. Borgelt (Eds.),
Frontiers in Computational Intelligence, Springer, 2018, pp. 31–46.'
date_created: 2019-07-10T15:39:00Z
date_updated: 2022-01-06T06:50:50Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
editor:
- first_name: Sanaz
full_name: Mostaghim, Sanaz
last_name: Mostaghim
- first_name: Andreas
full_name: Nürnberger, Andreas
last_name: Nürnberger
- first_name: Christian
full_name: Borgelt, Christian
last_name: Borgelt
language:
- iso: eng
page: 31-46
publication: Frontiers in Computational Intelligence
publisher: Springer
status: public
title: 'Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based
estimators'
type: book_chapter
user_id: '49109'
year: '2018'
...
---
_id: '16038'
author:
- first_name: D.
full_name: Schäfer, D.
last_name: Schäfer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Schäfer D, Hüllermeier E. Dyad ranking using Plackett-Luce models based on
joint feature representations. Machine Learning. 2018;107(5):903-941.
apa: Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce
models based on joint feature representations. Machine Learning, 107(5),
903–941.
bibtex: '@article{Schäfer_Hüllermeier_2018, title={Dyad ranking using Plackett-Luce
models based on joint feature representations}, volume={107}, number={5}, journal={Machine
Learning}, author={Schäfer, D. and Hüllermeier, Eyke}, year={2018}, pages={903–941}
}'
chicago: 'Schäfer, D., and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models
Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018):
903–41.'
ieee: D. Schäfer and E. Hüllermeier, “Dyad ranking using Plackett-Luce models based
on joint feature representations,” Machine Learning, vol. 107, no. 5, pp.
903–941, 2018.
mla: Schäfer, D., and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models
Based on Joint Feature Representations.” Machine Learning, vol. 107, no.
5, 2018, pp. 903–41.
short: D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.
date_created: 2020-02-24T15:59:19Z
date_updated: 2022-01-06T06:52:42Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
intvolume: ' 107'
issue: '5'
language:
- iso: eng
page: 903-941
publication: Machine Learning
status: public
title: Dyad ranking using Plackett-Luce models based on joint feature representations
type: journal_article
user_id: '49109'
volume: 107
year: '2018'
...
---
_id: '10145'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning.
In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.'
apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on
Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence
(AAAI) (pp. 2951–2958).
bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_2018, title={Learning to Rank
Based on Analogical Reasoning}, booktitle={Proc. 32 nd AAAI Conference on Artificial
Intelligence (AAAI)}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke},
year={2018}, pages={2951–2958} }'
chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based
on Analogical Reasoning.” In Proc. 32 Nd AAAI Conference on Artificial Intelligence
(AAAI), 2951–58, 2018.
ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank Based on Analogical
Reasoning,” in Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI),
2018, pp. 2951–2958.
mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical
Reasoning.” Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI),
2018, pp. 2951–58.
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. 32 Nd AAAI Conference on Artificial
Intelligence (AAAI), 2018, pp. 2951–2958.'
date_created: 2019-06-07T08:49:33Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
language:
- iso: eng
page: 2951-2958
publication: Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)
status: public
title: Learning to Rank Based on Analogical Reasoning
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '10148'
author:
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Robert
full_name: Busa-Fekete, Robert
last_name: Busa-Fekete
citation:
ama: 'El Mesaoudi-Paul A, Hüllermeier E, Busa-Fekete R. Ranking Distributions based
on Noisy Sorting. In: Proc. 35th Int. Conference on Machine Learning (ICML).
Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe
des Heinz Nixdorf Instituts, Paderborn; 2018:3469-3477.'
apa: El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking
Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine
Learning (ICML), 3469–3477.
bibtex: '@inproceedings{El Mesaoudi-Paul_Hüllermeier_Busa-Fekete_2018, series={Verlagsschriftenreihe
des Heinz Nixdorf Instituts, Paderborn}, title={Ranking Distributions based on
Noisy Sorting}, booktitle={Proc. 35th Int. Conference on Machine Learning (ICML)},
publisher={Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}, author={El
Mesaoudi-Paul, Adil and Hüllermeier, Eyke and Busa-Fekete, Robert}, year={2018},
pages={3469–3477}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts,
Paderborn} }'
chicago: El Mesaoudi-Paul, Adil, Eyke Hüllermeier, and Robert Busa-Fekete. “Ranking
Distributions Based on Noisy Sorting.” In Proc. 35th Int. Conference on Machine
Learning (ICML), 3469–77. Verlagsschriftenreihe Des Heinz Nixdorf Instituts,
Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018.
ieee: A. El Mesaoudi-Paul, E. Hüllermeier, and R. Busa-Fekete, “Ranking Distributions
based on Noisy Sorting,” in Proc. 35th Int. Conference on Machine Learning
(ICML), 2018, pp. 3469–3477.
mla: El Mesaoudi-Paul, Adil, et al. “Ranking Distributions Based on Noisy Sorting.”
Proc. 35th Int. Conference on Machine Learning (ICML), Verlagsschriftenreihe
des Heinz Nixdorf Instituts, Paderborn, 2018, pp. 3469–77.
short: 'A. El Mesaoudi-Paul, E. Hüllermeier, R. Busa-Fekete, in: Proc. 35th Int.
Conference on Machine Learning (ICML), Verlagsschriftenreihe des Heinz Nixdorf
Instituts, Paderborn, 2018, pp. 3469–3477.'
date_created: 2019-06-07T09:02:37Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
language:
- iso: eng
page: 3469-3477
publication: Proc. 35th Int. Conference on Machine Learning (ICML)
publisher: Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn
status: public
title: Ranking Distributions based on Noisy Sorting
type: conference
user_id: '5786'
year: '2018'
...
---
_id: '10149'
author:
- first_name: M.
full_name: Hesse, M.
last_name: Hesse
- first_name: J.
full_name: Timmermann, J.
last_name: Timmermann
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
last_name: Trächtler
citation:
ama: 'Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning
Strategy for the Swing-Up of the Double Pendulum on a Cart. In: Proc. 4th Int.
Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected
Systems in Products and Production, Procedia Manufacturing 24. ; 2018:15-20.'
apa: 'Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A
Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.
Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible
and Connected Systems in Products and Production, Procedia Manufacturing 24,
15–20.'
bibtex: '@inproceedings{Hesse_Timmermann_Hüllermeier_Trächtler_2018, title={A Reinforcement
Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}, booktitle={Proc.
4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and
Connected Systems in Products and Production, Procedia Manufacturing 24}, author={Hesse,
M. and Timmermann, J. and Hüllermeier, Eyke and Trächtler, Ansgar}, year={2018},
pages={15–20} }'
chicago: 'Hesse, M., J. Timmermann, Eyke Hüllermeier, and Ansgar Trächtler. “A Reinforcement
Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” In Proc.
4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and
Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20,
2018.'
ieee: 'M. Hesse, J. Timmermann, E. Hüllermeier, and A. Trächtler, “A Reinforcement
Learning Strategy for the Swing-Up of the Double Pendulum on a Cart,” in Proc.
4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and
Connected Systems in Products and Production, Procedia Manufacturing 24, 2018,
pp. 15–20.'
mla: 'Hesse, M., et al. “A Reinforcement Learning Strategy for the Swing-Up of the
Double Pendulum on a Cart.” Proc. 4th Int. Conference on System-Integrated
Intelligence: Intelligent, Flexible and Connected Systems in Products and Production,
Procedia Manufacturing 24, 2018, pp. 15–20.'
short: 'M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, in: Proc. 4th Int.
Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected
Systems in Products and Production, Procedia Manufacturing 24, 2018, pp. 15–20.'
date_created: 2019-06-07T09:10:51Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
language:
- iso: eng
page: 15-20
publication: 'Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent,
Flexible and Connected Systems in Products and Production, Procedia Manufacturing
24'
status: public
title: A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on
a Cart
type: conference
user_id: '5786'
year: '2018'
...
---
_id: '10152'
author:
- first_name: E.Loza
full_name: Mencia, E.Loza
last_name: Mencia
- first_name: J.
full_name: Fürnkranz, J.
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: M.
full_name: Rapp, M.
last_name: Rapp
citation:
ama: 'Mencia EL, Fürnkranz J, Hüllermeier E, Rapp M. Learning interpretable rules
for multi-label classification. In: Jair Escalante H, Escalera S, Guyon I, et
al., eds. Explainable and Interpretable Models in Computer Vision and Machine
Learning. The Springer Series on Challenges in Machine Learning. Springer;
2018:81-113.'
apa: Mencia, E. L., Fürnkranz, J., Hüllermeier, E., & Rapp, M. (2018). Learning
interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera,
I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, & M. A. J. van Gerven (Eds.),
Explainable and Interpretable Models in Computer Vision and Machine Learning
(pp. 81–113). Springer.
bibtex: '@inbook{Mencia_Fürnkranz_Hüllermeier_Rapp_2018, series={The Springer Series
on Challenges in Machine Learning}, title={Learning interpretable rules for multi-label
classification}, booktitle={Explainable and Interpretable Models in Computer Vision
and Machine Learning}, publisher={Springer}, author={Mencia, E.Loza and Fürnkranz,
J. and Hüllermeier, Eyke and Rapp, M.}, editor={Jair Escalante, H. and Escalera,
S. and Guyon, I. and Baro, X. and Güclüütürk, Y. and Güclü, U. and van Gerven,
M.A.J.Editors}, year={2018}, pages={81–113}, collection={The Springer Series on
Challenges in Machine Learning} }'
chicago: Mencia, E.Loza, J. Fürnkranz, Eyke Hüllermeier, and M. Rapp. “Learning
Interpretable Rules for Multi-Label Classification.” In Explainable and Interpretable
Models in Computer Vision and Machine Learning, edited by H. Jair Escalante,
S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, and M.A.J. van Gerven,
81–113. The Springer Series on Challenges in Machine Learning. Springer, 2018.
ieee: E. L. Mencia, J. Fürnkranz, E. Hüllermeier, and M. Rapp, “Learning interpretable
rules for multi-label classification,” in Explainable and Interpretable Models
in Computer Vision and Machine Learning, H. Jair Escalante, S. Escalera, I.
Guyon, X. Baro, Y. Güclüütürk, U. Güclü, and M. A. J. van Gerven, Eds. Springer,
2018, pp. 81–113.
mla: Mencia, E. Loz., et al. “Learning Interpretable Rules for Multi-Label Classification.”
Explainable and Interpretable Models in Computer Vision and Machine Learning,
edited by H. Jair Escalante et al., Springer, 2018, pp. 81–113.
short: 'E.L. Mencia, J. Fürnkranz, E. Hüllermeier, M. Rapp, in: H. Jair Escalante,
S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, M.A.J. van Gerven (Eds.),
Explainable and Interpretable Models in Computer Vision and Machine Learning,
Springer, 2018, pp. 81–113.'
date_created: 2019-06-07T09:17:56Z
date_updated: 2022-01-06T06:50:31Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
- _id: '26'
editor:
- first_name: H.
full_name: Jair Escalante, H.
last_name: Jair Escalante
- first_name: S.
full_name: Escalera, S.
last_name: Escalera
- first_name: I.
full_name: Guyon, I.
last_name: Guyon
- first_name: X.
full_name: Baro, X.
last_name: Baro
- first_name: Y.
full_name: Güclüütürk, Y.
last_name: Güclüütürk
- first_name: U.
full_name: Güclü, U.
last_name: Güclü
- first_name: M.A.J.
full_name: van Gerven, M.A.J.
last_name: van Gerven
language:
- iso: eng
page: 81-113
publication: Explainable and Interpretable Models in Computer Vision and Machine Learning
publisher: Springer
series_title: The Springer Series on Challenges in Machine Learning
status: public
title: Learning interpretable rules for multi-label classification
type: book_chapter
user_id: '49109'
year: '2018'
...