Generalized Naive Bayes Classifiers

Authors:
Kim Larsen
Affiliations:
Concord, California
Venue:
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Year:
2005

Citing 1
Cited 1

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Abstract

This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and predictive analysis. It can generate accurate predictions through a flexible, non-parametric fitting procedure, while being able to uncover hidden patterns in the data. In this paper, the Generalized Naive Bayes Classifier and the original Bayes Classifier will be demonstrated. Also, important ties to logistic regression, the Generalized Additive Model (GAM), and Weight Of Evidence will be discussed.