--- _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' ...