A Selective Naïve Bayesian Classification Algorithm Based on Rough Set
Yin E Hu, Ke Luo · Applied Mechanics and Materials · 2013
Naive Bayesian classifier (NBC) is a simple and effective classification model, but its condition independence assumption is often violated in reality and makes it perform poorly. In our study, we attempt to improve the NBC model through the way of attribute selection based on rough set. The main idea of the improvement model is to select a closest approximate independent attributes subset and relax the assumption of independence. Through the experimental comparison and analysis on the UCI datasets, the model is proved effective.