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On Learning of Choice Models with Interactive Attributes

On Learning of Choice Models with Interactive Attributes

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On Learning of Choice Models with Interactive Attributes

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 DMís 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 DMís preference model in terms of theparameters 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.

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  • Model: PROJ7801
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Friday 11 August, 2017.