A Decomposition Of Classes Via Clustering To Explain And Improve Naive Bayes

We propose a method to improve the probability estimates made by Naive Bayes to avoid the e ects of poor class conditional probabilities based on product distributions when each class spreads into multiple regions. Our approach is based on applying a clustering algorithm to each subset of examples that belong to the same class, and to consider each cluster as a class of its own. Experiments on 26 real-world datasets show a signi cant improvement in performance when the class decomposition process is applied, particularly when the mean number of clusters per class is large.

By: Ricardo Vilalta, Irina Rish

Published in: RC22852 in 2003


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