Scaling-invariant maximum margin preference learning

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dc.contributor.author Montazery, Mojtaba
dc.contributor.author Wilson, Nic
dc.date.accessioned 2020-12-02T11:19:25Z
dc.date.available 2020-12-02T11:19:25Z
dc.date.issued 2020-10-19
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.volume 128 en
dc.identifier.startpage 69 en
dc.identifier.endpage 101 en
dc.identifier.issn 0888-613X
dc.identifier.uri http://hdl.handle.net/10468/10802
dc.identifier.doi 10.1016/j.ijar.2020.10.006 en
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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier 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
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-11-04T11:58:30Z
dc.description.version Published Version en
dc.internal.rssid 542655603
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder European Regional Development Fund en
dc.description.status Peer reviewed en
dc.identifier.journaltitle International Journal of Approximate Reasoning en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress n.wilson@ucc.ie 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


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© 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/) Except where otherwise noted, this item's license is described as © 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/)
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