Semi-supervised Learning for Discrete Choice Models

02/16/2017
by   Jie Yang, et al.
0

We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated 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 adjust the number of clusters. Two computational studies including 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.

READ FULL TEXT

page 13

page 15

research
06/20/2012

Analysis of Semi-Supervised Learning with the Yarowsky Algorithm

The Yarowsky algorithm is a rule-based semi-supervised learning algorith...
research
05/02/2018

COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

Clustering is ubiquitous in data analysis, including analysis of time se...
research
11/20/2021

Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training

Seismic impedance inversion can be performed with a semi-supervised lear...
research
07/01/2019

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

In the recent years, there is a growing interest in semi-supervised lear...
research
03/21/2021

ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning

In this paper, we address the problem of training deep neural networks i...
research
02/12/2020

Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise

Semi-supervised learning methods are usually employed in the classificat...
research
06/28/2022

Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)

Semi-supervised learning is the problem of training an accurate predicti...

Please sign up or login with your details

Forgot password? Click here to reset