Classification Algorithm based on NB for Class Overlapping Problem
Haitao Xiong, Ming Li, Tongqiang Jiang, Shouxiang Zhao · Applied Mathematics & Information Sciences · 2013
Abstract: Class overlapping is thought as one of the toughest problems in data mining because the complex structure of data. The current classification algorithms show little consideration of this problem. So when using this traditional classification algorithms to resolve this problem, classification performance is not good for samples in overlapping region. To meet this critical challenge, in this paper, we pay a systematic study on the class overlapping problem and propose a new classification algorithm based on NB for class overlapping problem (CANB). CANB uses NB to find class overlapping region and use this region and non-overlapping region in NB classification model learning separately. Experimental results on bench mark and real-world data sets demonstrate that CANB can improve the classification performances for class overlapping problem stably and effectively.