Semi-Supervised Learning for Discrete Choice Models

We introduce a semi-supervised discrete choice model with algorithmic approaches to estimate choice models when relatively few requests have actual preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2019-11, Vol.20 (11), p.4145-4159
Main Authors: Yang, Jie, Shebalov, Sergey, Klabjan, Diego
Format: Article
Language:English
Subjects:
Adaptation models
Algorithms
Atmospheric modeling
Biological system modeling
Clustering
Clustering algorithms
Computational modeling
Data models
discrete choice models
Economic forecasting
Economic models
Machine learning
Prediction algorithms
Semi-supervised learning
Shopping
Travel demand
travel demand forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce a semi-supervised discrete choice model with algorithmic approaches to estimate choice models when relatively few requests have actual preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label algorithm, have been adapted to our choice modeling problem setting. We also develop two new algorithms based on the cluster-and-label algorithm. The new algorithms use the Bayesian Information Criterion to evaluate a clustering setting to automatically generate new clusters out of existing clusters and adjust the number of clusters. Two computational studies focusing on travel demand forecasting (i.e., a hotel booking case and a large-scale airline itinerary shopping case) are presented to evaluate the prediction accuracy and computational effort of the proposed algorithms. Algorithmic recommendations are rendered under various scenarios based on the hotel booking case while economic insights are derived based on the itinerary shopping case.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2882847