Combining attributes to improve the performance of Naive Bayes for Regression

Naive Bayes for regression (NBR) uses the naive Bayes methodology to numeric prediction tasks. The main reason for its poor performance is the independence assumption. Although many recent researches try to improve the performance of naive Bayes by relaxing the independence assumption, none of them can be directly applied to the regression framework. The objective of this work is to present a new approach to improve the results of the NBR algorithm, by combining attributes by means of an auxiliary regression algorithm.