ArXiv:1812.09747
Sifringer, Brian
Lurkin, Virginie
Alahi, Alexandre
Let Me Not Lie: Learning MultiNomial Logit
http://infoscience.epfl.ch/record/262662/files/1812.09747.pdf
Discrete choice models generally assume that model specification is known a priori. In practice, determining the utility specification for a particular application remains a difficult task and model misspecification may lead to biased parameter estimates. In this paper, we propose a new mathematical framework for estimating choice models in which the systematic part of the utility specification is divided into an interpretable part and a learning representation part that aims at automatically discovering a good utility specification from available data. We show the effectiveness of our framework by augmenting the utility specification of the Multinomial Logit Model (MNL) with a new non-linear representation arising from a Neural Network (NN). This leads to a new choice model referred to as the Learning Multinomial Logit (L-MNL) model. Our experiments show that our L-MNL model outperformed the traditional MNL models and existing hybrid neural network models both in terms of predictive performance and accuracy in parameter estimation.
2018-12-28T11:58:54Z
http://infoscience.epfl.ch/record/262662
http://infoscience.epfl.ch/record/262662
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