Scaling-invariant maximum margin preference learning
| dc.contributor.author | Montazery, Mojtaba | |
| dc.contributor.author | Wilson, Nic | |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.contributor.funder | European Regional Development Fund | en |
| dc.date.accessioned | 2020-12-02T11:19:25Z | |
| dc.date.available | 2020-12-02T11:19:25Z | |
| dc.date.issued | 2020-10-19 | |
| dc.date.updated | 2020-11-04T11:58:30Z | |
| dc.description.abstract | One natural way to express preferences over items is to represent them in the form of pairwise comparisons, from which a model is learned in order to predict further preferences. In this setting, if an item a is preferred to the item b, then it is natural to consider that the preference still holds after multiplying both vectors by a positive scalar (e.g., ). Such invariance to scaling is satisfied in maximum margin learning approaches for pairs of test vectors, but not for the preference input pairs, i.e., scaling the inputs in a different way could result in a different preference relation being learned. In addition to the scaling of preference inputs, maximum margin methods are also sensitive to the way used for normalizing (scaling) the features, which is an essential pre-processing phase for these methods. In this paper, we define and analyse more cautious preference relations that are invariant to the scaling of features, or preference inputs, or both simultaneously; this leads to computational methods for testing dominance with respect to the induced relations, and for generating optimal solutions (i.e., best items) among a set of alternatives. In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set. | en |
| dc.description.sponsorship | Science Foundation Ireland (SFI under Grant Numbers SFI/12/RC/2289 and 12/RC/2289-P2 ,co-funded under the European Regional Development Fund) | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Montazery, M. and Wilson, N. (2021) 'Scaling-invariant maximum margin preference learning', International Journal of Approximate Reasoning, 128, pp. 69-101. doi: 10.1016/j.ijar.2020.10.006 | en |
| dc.identifier.doi | 10.1016/j.ijar.2020.10.006 | en |
| dc.identifier.endpage | 101 | en |
| dc.identifier.issn | 0888-613X | |
| dc.identifier.journaltitle | International Journal of Approximate Reasoning | en |
| dc.identifier.startpage | 69 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/10802 | |
| dc.identifier.volume | 128 | en |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en |
| dc.relation.uri | https://www.sciencedirect.com/science/article/pii/S0888613X20302401 | |
| dc.rights | © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Preference learning | en |
| dc.subject | Preference inference | en |
| dc.title | Scaling-invariant maximum margin preference learning | en |
| dc.type | Article (peer-reviewed) | en |
