Multiple-Instance Logistic Regression with LASSO Penalty
Machine Learning
2016-07-14 v1 Applications
Abstract
In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the important covariates by adding the coefficient penalty term into the likelihood function. In addition to essential technical details, we demonstrate the usefulness of the proposed method by simulations and real examples.
Cite
@article{arxiv.1607.03615,
title = {Multiple-Instance Logistic Regression with LASSO Penalty},
author = {Ray-Bing Chen and Kuang-Hung Cheng and Sheng-Mao Chang and Shuen-Lin Jeng and Ping-Yang Chen and Chun-Hao Yang and Chi-Chun Hsia},
journal= {arXiv preprint arXiv:1607.03615},
year = {2016}
}