---
_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: '5693'
author:
- first_name: Helena
full_name: Graf, Helena
id: '52640'
last_name: Graf
citation:
ama: Graf H. Ranking of Classification Algorithms in AutoML. Universität
Paderborn; 2018.
apa: Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität
Paderborn.
bibtex: '@book{Graf_2018, title={Ranking of Classification Algorithms in AutoML},
publisher={Universität Paderborn}, author={Graf, Helena}, year={2018} }'
chicago: Graf, Helena. Ranking of Classification Algorithms in AutoML. Universität
Paderborn, 2018.
ieee: H. Graf, Ranking of Classification Algorithms in AutoML. Universität
Paderborn, 2018.
mla: Graf, Helena. Ranking of Classification Algorithms in AutoML. Universität
Paderborn, 2018.
short: H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn,
2018.
date_created: 2018-11-15T08:06:41Z
date_updated: 2022-01-06T07:02:35Z
department:
- _id: '355'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Ranking of Classification Algorithms in AutoML
type: bachelorsthesis
user_id: '33176'
year: '2018'
...
---
_id: '5936'
author:
- first_name: Manuel
full_name: Scheibl, Manuel
last_name: Scheibl
citation:
ama: Scheibl M. Learning about Learning Curves from Dataset Properties. Universität
Paderborn; 2018.
apa: Scheibl, M. (2018). Learning about learning curves from dataset properties.
Universität Paderborn.
bibtex: '@book{Scheibl_2018, title={Learning about learning curves from dataset
properties}, publisher={Universität Paderborn}, author={Scheibl, Manuel}, year={2018}
}'
chicago: Scheibl, Manuel. Learning about Learning Curves from Dataset Properties.
Universität Paderborn, 2018.
ieee: M. Scheibl, Learning about learning curves from dataset properties.
Universität Paderborn, 2018.
mla: Scheibl, Manuel. Learning about Learning Curves from Dataset Properties.
Universität Paderborn, 2018.
short: M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität
Paderborn, 2018.
date_created: 2018-11-28T10:29:53Z
date_updated: 2022-01-06T07:02:47Z
department:
- _id: '355'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Learning about learning curves from dataset properties
type: bachelorsthesis
user_id: '477'
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'
...