Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/19470
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dc.contributor.authorAggarwal, Manish-
dc.date.accessioned2017-06-22T09:08:22Z-
dc.date.available2017-06-22T09:08:22Z-
dc.date.issued2016-
dc.identifier.citationAggarwal M. (2016). On learning of choice models with interactive attributes. IEEE Transactions on Knowledge and Data Engineering, 28(10), 2697-2708.en_US
dc.identifier.urihttp://hdl.handle.net/11718/19470-
dc.description.abstractIntroducing recent advances in the machine learning techniques to state-of-the-art discrete choice models, we develop an approach to infer the unique and complex decision making process of a decision-maker (DM), which is characterized by the DM's priorities and attitudinal character, along with the attributes interaction, to name a few. On the basis of exemplary preference information in the form of pairwise comparisons of alternatives, our method seeks to induce a DM's preference model in terms of the parameters of recent discrete choice models. To this end, we reduce our learning function to a constrained non-linear optimization problem. Our learning approach is a simple one that takes into consideration the interaction among the attributes along with the priorities and the unique attitudinal character of a DM. The experimental results on standard benchmark datasets suggest that our approach is not only intuitively appealing and easily interpretable but also competitive to state-of-the-art methods.en_US
dc.language.isoen_USen_US
dc.publisherIEEE Computer Societyen_US
dc.subjectAttitudinal characteren_US
dc.subjectAttributes interactionen_US
dc.subjectChoice modellingen_US
dc.subjectMulti-attribute decision makingen_US
dc.subjectPreference learningen_US
dc.titleOn learning of choice models with interactive attributesen_US
dc.typeArticleen_US
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