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DC Field | Value | Language |
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dc.contributor.author | Aggarwal, Manish | - |
dc.date.accessioned | 2017-06-22T09:08:22Z | - |
dc.date.available | 2017-06-22T09:08:22Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Aggarwal 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.uri | http://hdl.handle.net/11718/19470 | - |
dc.description.abstract | Introducing 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.iso | en_US | en_US |
dc.publisher | IEEE Computer Society | en_US |
dc.subject | Attitudinal character | en_US |
dc.subject | Attributes interaction | en_US |
dc.subject | Choice modelling | en_US |
dc.subject | Multi-attribute decision making | en_US |
dc.subject | Preference learning | en_US |
dc.title | On learning of choice models with interactive attributes | en_US |
dc.type | Article | en_US |
Appears in Collections: | Journal Articles |
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File | Description | Size | Format | |
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On Learning of Choice-Manish Aggarwal-ITKDE-2016.pdf Restricted Access | 259.12 kB | Adobe PDF | View/Open Request a copy |
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