A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization

TL;DR

This paper introduces a flexible Bayesian generalized linear model framework that encompasses probit and logistic regression, providing an EM algorithm for parameter estimation, and demonstrates superior performance in text categorization tasks compared to existing models.

Contribution

The paper proposes a novel class of sparse Bayesian generalized linear models with an EM algorithm for efficient learning, improving over traditional logistic and probit models.

Findings

Outperforms logistic and probit models in text classification

Shows significant improvement over elastic net in experiments

Effective in both simulated and real text data

Abstract

We present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that our method outperforms the logistic and probit models and also the elastic net, in general by a substantial margin.