Locally Weighted Naive Bayes
This incremental improvement addresses a specific limitation for users of naive Bayes classifiers, offering a simple and effective enhancement.
The paper tackles the attribute independence weakness of naive Bayes by introducing a locally weighted version that learns local models at prediction time, resulting in dramatic accuracy improvements in many cases without degrading performance compared to standard naive Bayes.
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.