Feature subset selection based on max mutual information and max correlation entropy
Zifei Han · Jisuanji yingyong yanjiu · 2009
Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model.A great many algorithms had been proposed,but the problems of weak process ability and low classification accuracy still exists.To solve these problems,this paper proposed a max mutual information and max correlation entropy criterion based on fuzzy rough information entropy model and designed a new feature selection algorithm based on this criterion.The algorithm can deal with discrete data,continuous data,fuzzy and hybrid data.According to tests on UCI datasets,the algorithm is effective,and has higher accuracy and stability compared to three other common feature selection algorithms.