---
_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: '18014'
author:
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Dimitri
full_name: Weiß, Dimitri
last_name: Weiß
- 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: 'El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based
Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning
and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer
Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22'
apa: 'El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney,
K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit
Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096,
pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22'
bibtex: '@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham},
series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm
Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={10.1007/978-3-030-53552-0_22},
booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer},
author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier,
Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes
in Computer Science} }'
chicago: 'El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier,
and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection
Bandit Approach.” In Learning and Intelligent Optimization. LION 2020.,
12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-53552-0_22.'
ieee: 'A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based
Realtime Algorithm Configuration: A Preselection Bandit Approach,” in Learning
and Intelligent Optimization. LION 2020., vol. 12096, Cham: Springer, 2020,
pp. 216–232.'
mla: 'El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration:
A Preselection Bandit Approach.” Learning and Intelligent Optimization. LION
2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.'
short: 'A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in:
Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.'
date_created: 2020-08-17T11:44:37Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
doi: 10.1007/978-3-030-53552-0_22
intvolume: ' 12096'
language:
- iso: eng
page: 216 - 232
place: Cham
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Learning and Intelligent Optimization. LION 2020.
publication_identifier:
isbn:
- '9783030535513'
- '9783030535520'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach'
type: book_chapter
user_id: '76599'
volume: 12096
year: '2020'
...
---
_id: '18017'
abstract:
- lang: eng
text: "We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich,
instead of selecting a single alternative (arm), a learner is supposed\r\nto make
a preselection in the form of a subset of alternatives. More\r\nspecifically,
in each iteration, the learner is presented a set of arms and a\r\ncontext, both
described in terms of feature vectors. The task of the learner is\r\nto preselect
$k$ of these arms, among which a final choice is made in a second\r\nstep. In
our setup, we assume that each arm has a latent (context-dependent)\r\nutility,
and that feedback on a preselection is produced according to a\r\nPlackett-Luce
model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB
algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular,
we consider an online algorithm selection scenario, which\r\nserved as a main
motivation of our problem setting. Here, an instance (which\r\ndefines the context)
from a certain problem class (such as SAT) can be solved\r\nby different algorithms
(the arms), but only $k$ of these algorithms can\r\nactually be run."
author:
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context
Information under the Plackett-Luce Model. arXiv:200204275.
apa: El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection
with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.
bibtex: '@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection
with Context Information under the Plackett-Luce Model}, journal={arXiv:2002.04275},
author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }'
chicago: El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection
with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275,
n.d.
ieee: A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with
Context Information under the Plackett-Luce Model,” arXiv:2002.04275.
.
mla: El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information
under the Plackett-Luce Model.” ArXiv:2002.04275.
short: A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).
date_created: 2020-08-17T11:49:40Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:2002.04275
publication_status: draft
status: public
title: Online Preselection with Context Information under the Plackett-Luce Model
type: preprint
user_id: '76599'
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: '16725'
author:
- first_name: Cedric
full_name: Richter, Cedric
id: '50003'
last_name: Richter
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software
Validation Based on Graph Kernels. Journal of Automated Software Engineering.
apa: Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm
Selection for Software Validation Based on Graph Kernels. Journal of Automated
Software Engineering.
bibtex: '@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection
for Software Validation Based on Graph Kernels}, journal={Journal of Automated
Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier,
Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }'
chicago: Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim.
“Algorithm Selection for Software Validation Based on Graph Kernels.” Journal
of Automated Software Engineering, n.d.
ieee: C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection
for Software Validation Based on Graph Kernels,” Journal of Automated Software
Engineering.
mla: Richter, Cedric, et al. “Algorithm Selection for Software Validation Based
on Graph Kernels.” Journal of Automated Software Engineering, Springer.
short: C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated
Software Engineering (n.d.).
date_created: 2020-04-19T14:08:06Z
date_updated: 2022-01-06T06:52:55Z
department:
- _id: '7'
- _id: '77'
- _id: '355'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
publication: Journal of Automated Software Engineering
publication_status: accepted
publisher: Springer
status: public
title: Algorithm Selection for Software Validation Based on Graph Kernels
type: journal_article
user_id: '477'
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'
...
---
_id: '15025'
abstract:
- lang: eng
text: In software engineering, the imprecise requirements of a user are transformed
to a formal requirements specification during the requirements elicitation process.
This process is usually guided by requirements engineers interviewing the user.
We want to partially automate this first step of the software engineering process
in order to enable users to specify a desired software system on their own. With
our approach, users are only asked to provide exemplary behavioral descriptions.
The problem of synthesizing a requirements specification from examples can partially
be reduced to the problem of grammatical inference, to which we apply an active
coevolutionary learning approach. However, this approach would usually require
many feedback queries to be sent to the user. In this work, we extend and generalize
our active learning approach to receive knowledge from multiple oracles, also
known as proactive learning. The ‘user oracle’ represents input received from
the user and the ‘knowledge oracle’ represents available, formalized domain knowledge.
We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q)
algorithm. We compare FAKT/Q to the active learning approach and provide an extensive
benchmark evaluation. As result we find that the number of required user queries
is reduced and the inference process is sped up significantly. Finally, with so-called
On-The-Fly Markets, we present a motivation and an application of our approach
where such knowledge is available.
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: Lorijn
full_name: van Rooijen, Lorijn
id: '58843'
last_name: van Rooijen
- first_name: Heiko
full_name: Hamann, Heiko
last_name: Hamann
citation:
ama: Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of
Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266
apa: Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266
bibtex: '@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets},
volume={28}, DOI={10.1162/evco_a_00266},
number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals},
author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020},
pages={165–193} }'
chicago: 'Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle
Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly
Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.'
ieee: 'M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets,”
Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.'
mla: Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements
Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation,
vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.
short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020)
165–193.
date_created: 2019-11-18T14:19:19Z
date_updated: 2022-01-06T06:52:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
- _id: '63'
- _id: '238'
doi: 10.1162/evco_a_00266
intvolume: ' 28'
issue: '2'
language:
- iso: eng
page: 165–193
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Evolutionary Computation
publication_status: published
publisher: MIT Press Journals
related_material:
link:
- relation: confirmation
url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266
status: public
title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples
in On-The-Fly Markets
type: journal_article
user_id: '15415'
volume: 28
year: '2020'
...
---
_id: '19523'
abstract:
- lang: eng
text: "We study the problem of learning choice functions, which play an important\r\nrole
in various domains of application, most notably in the field of economics.\r\nFormally,
a choice function is a mapping from sets to sets: Given a set of\r\nchoice alternatives
as input, a choice function identifies a subset of most\r\npreferred elements.
Learning choice functions from suitable training data comes\r\nwith a number of
challenges. For example, the sets provided as input and the\r\nsubsets produced
as output can be of any size. Moreover, since the order in\r\nwhich alternatives
are presented is irrelevant, a choice function should be\r\nsymmetric. Perhaps
most importantly, choice functions are naturally\r\ncontext-dependent, in the
sense that the preference in favor of an alternative\r\nmay depend on what other
options are available. We formalize the problem of\r\nlearning choice functions
and present two general approaches based on two\r\nrepresentations of context-dependent
utility functions. Both approaches are\r\ninstantiated by means of appropriate
neural network architectures, and their\r\nperformance is demonstrated on suitable
benchmark tasks."
author:
- first_name: Karlson
full_name: Pfannschmidt, Karlson
last_name: Pfannschmidt
- first_name: Pritha
full_name: Gupta, Pritha
last_name: Gupta
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts
and Architectures. arXiv:190110860. 2019.'
apa: 'Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice
Functions: Concepts and Architectures. ArXiv:1901.10860.'
bibtex: '@article{Pfannschmidt_Gupta_Hüllermeier_2019, title={Learning Choice Functions:
Concepts and Architectures}, journal={arXiv:1901.10860}, author={Pfannschmidt,
Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice
Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.'
ieee: 'K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions:
Concepts and Architectures,” arXiv:1901.10860. 2019.'
mla: 'Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.”
ArXiv:1901.10860, 2019.'
short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).
date_created: 2020-09-17T10:53:38Z
date_updated: 2022-01-06T06:54:06Z
department:
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:1901.10860
status: public
title: 'Learning Choice Functions: Concepts and Architectures'
type: preprint
user_id: '13472'
year: '2019'
...
---
_id: '17565'
author:
- first_name: Marie-Luis
full_name: Merten, Marie-Luis
last_name: Merten
- first_name: Nina
full_name: Seemann, Nina
last_name: Seemann
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches
Jahrbuch. 2019;(142):124-146.
apa: Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches
Jahrbuch, 142, 124–146.
bibtex: '@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff},
number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and
Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }'
chicago: 'Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel
digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im
interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019):
124–46.'
ieee: M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,”
Niederdeutsches Jahrbuch, no. 142, pp. 124–146, 2019.
mla: Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.”
Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46.
short: M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.
date_created: 2020-08-03T13:55:04Z
date_updated: 2022-01-06T06:53:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
issue: '142'
language:
- iso: ger
page: 124-146
project:
- _id: '39'
name: InterGramm
publication: Niederdeutsches Jahrbuch
publication_status: published
status: public
title: Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher
Sprachausbau im interdisziplinären Zugriff
type: journal_article
user_id: '5786'
year: '2019'
...
---
_id: '18018'
abstract:
- lang: eng
text: |-
A common statistical task lies in showing asymptotic normality of certain
statistics. In many of these situations, classical textbook results on weak
convergence theory suffice for the problem at hand. However, there are quite
some scenarios where stronger results are needed in order to establish an
asymptotic normal approximation uniformly over a family of probability
measures. In this note we collect some results in this direction. We restrict
ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures.
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory.
arXiv:190309864. 2019.
apa: Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak
convergence theory. ArXiv:1903.09864.
bibtex: '@article{Bengs_Holzmann_2019, title={Uniform approximation in classical
weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and
Holzmann, Hajo}, year={2019} }'
chicago: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
ieee: V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence
theory,” arXiv:1903.09864. 2019.
mla: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
short: V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
date_created: 2020-08-17T12:10:55Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
publication: arXiv:1903.09864
status: public
title: Uniform approximation in classical weak convergence theory
type: preprint
user_id: '76599'
year: '2019'
...
---
_id: '8868'
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
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning
for Multi-Label Classification. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated
Machine Learning for Multi-Label Classification. Presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated
Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik
and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer.
“Towards Automated Machine Learning for Multi-Label Classification,” 2019.
ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine
Learning for Multi-Label Classification,” presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany, 2019.
mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label
Classification. 2019.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.'
conference:
end_date: 2019-03-20
location: Bayreuth, Germany
name: European Conference on Data Analytics (ECDA)
start_date: 2019-03-18
date_created: 2019-04-10T07:17:55Z
date_updated: 2022-01-06T07:04:04Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2019-04-10T07:17:17Z
date_updated: 2019-04-10T07:17:17Z
file_id: '8870'
file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf
file_size: '74484'
relation: main_file
success: 1
file_date_updated: 2019-04-10T07:17:17Z
has_accepted_license: '1'
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
status: public
title: Towards Automated Machine Learning for Multi-Label Classification
type: conference_abstract
user_id: '49109'
year: '2019'
...
---
_id: '10578'
author:
- first_name: V. K.
full_name: Tagne, V. K.
last_name: Tagne
- first_name: S.
full_name: Fotso, S.
last_name: Fotso
- first_name: 'L. A. '
full_name: 'Fono, L. A. '
last_name: Fono
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows
and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
apa: Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice
Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics
and Natural Computation, 15(2), 191–213.
bibtex: '@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated
by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New
Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono,
L. A. and Hüllermeier, Eyke}, year={2019}, pages={191–213} }'
chicago: 'Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions
Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural
Computation 15, no. 2 (2019): 191–213.'
ieee: V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated
by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation,
vol. 15, no. 2, pp. 191–213, 2019.
mla: Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce
Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019,
pp. 191–213.
short: V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural
Computation 15 (2019) 191–213.
date_created: 2019-07-08T15:34:03Z
date_updated: 2022-01-06T06:50:45Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
intvolume: ' 15'
issue: '2'
language:
- iso: eng
page: 191-213
publication: New Mathematics and Natural Computation
status: public
title: Choice Functions Generated by Mallows and Plackett–Luce Relations
type: journal_article
user_id: '315'
volume: 15
year: '2019'
...
---
_id: '15001'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Christian
full_name: Borgelt, Christian
last_name: Borgelt
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Rudolf
full_name: Kruse, Rudolf
last_name: Kruse
citation:
ama: 'Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From
Statistical Foundations to Machine Learning. IEEE Computational Intelligence
Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642'
apa: 'Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets
in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational
Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642'
bibtex: '@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data
Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642},
journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt,
Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44}
}'
chicago: 'Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy
Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE
Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.'
ieee: 'I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis:
From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence
Magazine, pp. 31–44, 2019.'
mla: 'Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations
to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp.
31–44, doi:10.1109/mci.2018.2881642.'
short: I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence
Magazine (2019) 31–44.
date_created: 2019-11-15T10:11:37Z
date_updated: 2022-01-06T06:52:13Z
department:
- _id: '34'
- _id: '355'
doi: 10.1109/mci.2018.2881642
language:
- iso: eng
page: 31-44
publication: IEEE Computational Intelligence Magazine
publication_identifier:
issn:
- 1556-603X
- 1556-6048
publication_status: published
status: public
title: 'Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning'
type: journal_article
user_id: '315'
year: '2019'
...
---
_id: '15002'
abstract:
- lang: eng
text: Many problem settings in machine learning are concerned with the simultaneous
prediction of multiple target variables of diverse type. Amongst others, such
problem settings arise in multivariate regression, multi-label classification,
multi-task learning, dyadic prediction, zero-shot learning, network inference,
and matrix completion. These subfields of machine learning are typically studied
in isolation, without highlighting or exploring important relationships. In this
paper, we present a unifying view on what we call multi-target prediction (MTP)
problems and methods. First, we formally discuss commonalities and differences
between existing MTP problems. To this end, we introduce a general framework that
covers the above subfields as special cases. As a second contribution, we provide
a structured overview of MTP methods. This is accomplished by identifying a number
of key properties, which distinguish such methods and determine their suitability
for different types of problems. Finally, we also discuss a few challenges for
future research.
author:
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying
view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324.
doi:10.1007/s10618-018-0595-5'
apa: 'Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target
prediction: a unifying view on problems and methods. Data Mining and Knowledge
Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5'
bibtex: '@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction:
a unifying view on problems and methods}, volume={33}, DOI={10.1007/s10618-018-0595-5},
number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem
and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324}
}'
chicago: 'Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target
Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge
Discovery 33, no. 2 (2019): 293–324. https://doi.org/10.1007/s10618-018-0595-5.'
ieee: 'W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction:
a unifying view on problems and methods,” Data Mining and Knowledge Discovery,
vol. 33, no. 2, pp. 293–324, 2019.'
mla: 'Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems
and Methods.” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019,
pp. 293–324, doi:10.1007/s10618-018-0595-5.'
short: W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery
33 (2019) 293–324.
date_created: 2019-11-15T10:16:34Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
doi: 10.1007/s10618-018-0595-5
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:43:39Z
date_updated: 2020-02-28T12:45:26Z
file_id: '16155'
file_name: multi-target-prediction.pdf
file_size: 837808
relation: main_file
file_date_updated: 2020-02-28T12:45:26Z
has_accepted_license: '1'
intvolume: ' 33'
issue: '2'
language:
- iso: eng
oa: '1'
page: 293-324
publication: Data Mining and Knowledge Discovery
publication_identifier:
issn:
- 1573-756X
status: public
title: 'Multi-target prediction: a unifying view on problems and methods'
type: journal_article
user_id: '315'
volume: 33
year: '2019'
...
---
_id: '15003'
author:
- first_name: Thomas
full_name: Mortier, Thomas
last_name: Mortier
- first_name: Marek
full_name: Wydmuch, Marek
last_name: Wydmuch
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
citation:
ama: 'Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued
Prediction in Multi-Class Classification. In: Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019. ; 2019.'
apa: Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman,
W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings
of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the
28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels,
Belgium, November 6-8, 2019.
bibtex: '@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued
Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st
Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian
Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November
6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof
and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }'
chicago: Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier,
and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
ieee: T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued
Prediction in Multi-Class Classification,” in Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019, 2019.
mla: Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.”
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
short: 'T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in:
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.'
date_created: 2019-11-15T10:20:55Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
language:
- iso: eng
publication: Proceedings of the 31st Benelux Conference on Artificial Intelligence
{(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn
2019), Brussels, Belgium, November 6-8, 2019
status: public
title: Set-Valued Prediction in Multi-Class Classification
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15004'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning
to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22'
apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based
Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature
Selection for Analogy-Based Learning to Rank}, DOI={10.1007/978-3-030-33778-0_22},
booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier,
Eyke}, year={2019} }'
chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_22.
ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based
Learning to Rank,” in Discovery Science, Cham, 2019.
mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22.
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.'
date_created: 2019-11-15T10:24:45Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_22
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Feature Selection for Analogy-Based Learning to Rank
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15005'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels.
In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3'
apa: 'Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference
Learning with Kernels. In KI 2019: Advances in Artificial Intelligence.
Cham. https://doi.org/10.1007/978-3-030-30179-8_3'
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Analogy-Based
Preference Learning with Kernels}, DOI={10.1007/978-3-030-30179-8_3},
booktitle={KI 2019: Advances in Artificial Intelligence}, author={Ahmadi Fahandar,
Mohsen and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference
Learning with Kernels.” In KI 2019: Advances in Artificial Intelligence.
Cham, 2019. https://doi.org/10.1007/978-3-030-30179-8_3.'
ieee: 'M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning
with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.'
mla: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning
with Kernels.” KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.'
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial
Intelligence, Cham, 2019.'
date_created: 2019-11-15T10:30:10Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-30179-8_3
language:
- iso: eng
place: Cham
publication: 'KI 2019: Advances in Artificial Intelligence'
publication_identifier:
isbn:
- '9783030301781'
- '9783030301798'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Analogy-Based Preference Learning with Kernels
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15006'
author:
- first_name: Vu-Linh
full_name: Nguyen, Vu-Linh
last_name: Nguyen
- first_name: Sébastien
full_name: Destercke, Sébastien
last_name: Destercke
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In:
Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7'
apa: Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty
Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
bibtex: '@inbook{Nguyen_Destercke_Hüllermeier_2019, place={Cham}, title={Epistemic
Uncertainty Sampling}, DOI={10.1007/978-3-030-33778-0_7},
booktitle={Discovery Science}, author={Nguyen, Vu-Linh and Destercke, Sébastien
and Hüllermeier, Eyke}, year={2019} }'
chicago: Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic
Uncertainty Sampling.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_7.
ieee: V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,”
in Discovery Science, Cham, 2019.
mla: Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.” Discovery Science,
2019, doi:10.1007/978-3-030-33778-0_7.
short: 'V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham,
2019.'
date_created: 2019-11-15T10:35:08Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_7
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Epistemic Uncertainty Sampling
type: book_chapter
user_id: '49109'
year: '2019'
...
---
_id: '15007'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017'
apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101).
https://doi.org/10.1016/j.jmva.2019.02.017'
bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate:
Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017},
booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings
of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier,
Eyke}, year={2019} }'
chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101),
2019. https://doi.org/10.1016/j.jmva.2019.02.017.'
ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101), 2019.'
mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the
Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference
on Machine Learning (Proceedings of Machine Learning Research, 101), 2019,
doi:10.1016/j.jmva.2019.02.017.'
short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine
Learning (Proceedings of Machine Learning Research, 101), 2019.'
date_created: 2019-11-15T10:43:26Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1016/j.jmva.2019.02.017
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:47:07Z
date_updated: 2020-02-28T12:47:07Z
file_id: '16156'
file_name: learning-to-aggregate-owa.pdf
file_size: 2331320
relation: main_file
file_date_updated: 2020-02-28T12:47:07Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of
Machine Learning Research, 101)
publication_status: published
status: public
title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for
OWA'
type: conference
user_id: '477'
year: '2019'
...