Feature selection based on survival Cauchy-Schwartz mutual information
Feature selection techniques play a crucial role in machine learning tasks such as regression and classification. Many filter methods of feature selection are based on the mutual information (e.g. MIFS, MIFS-U, NMIFS, and mRMR methods). In this work, a new mutual information is defined based on the cross survival information potential (CSIP) and Cauchy-Schwartz divergence (CSD), called the survival Cauchy-Schwartz mutual information (SCS-MI). We apply this new mutual information to select an informative subset of features for a SVM classifier. Experimental results illustrate the desirable performance of the new method.