A semi-supervised adaptive discriminative discretization method improving discrimination power of regularized naive Bayes

Shihe Wang, Jianfeng Ren, Ruibin Bai

Abstract: Highlights•We identify the significant information loss in previous discretization methods.•We propose a Semi-supervised Adaptive Discriminative Discretization (SADD) method.•The proposed SADD is integrated with regularized naïve Bayes, namely RNB+.•The proposed SADD effectively enhances the discrimination power of NB classifiers.•Experimental results on 31 UCI datasets validate the effectiveness of proposed SADD.