Locally Weighted Naive Bayes

TL;DR

This paper introduces a locally weighted naive Bayes classifier that relaxes the attribute independence assumption by learning local models at prediction time, often improving accuracy with simplicity.

Contribution

It presents a novel locally weighted approach to naive Bayes that enhances performance while maintaining conceptual and computational simplicity.

Findings

Locally weighted naive Bayes rarely degrades accuracy.

In many cases, it dramatically improves accuracy.

The method is simple and computationally efficient.

Abstract

Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity.