A new feature selection algorithm based on relevance, redundancy and complementarity.

Defining important information from biological data is critical for the study of disease diagnosis, drug efficacy and individualized treatment. Hence, the feature selection technique is widely applied. Many feature selection methods measure features based on relevance, redundancy and complementarity. Feature complementarity means that two features' cooperation can provide more information than the simple summation of their individual information. In this paper, we studied the feature selection technique and proposed a new feature selection algorithm based on relevance, redundancy and complementarity (FS-RRC). On selecting the feature subset, FS-RRC not only evaluates the feature relevance with the class label and the redundancy among the features but also evaluates the feature complementarity. If complementary features exist for a selected relevant feature, FS-RRC retains the feature with the largest complementarity to the selected feature subset. To show the performance of FS-RRC, it was compared with eleven efficient feature selection methods, MIFS, mRMR, CMIM, ReliefF, FCBF, PGVNS, MCRMCR, MCRMICR, RCDFS, SAFE and SVM-RFE on two synthetic datasets and fifteen public biological datasets. The experimental results showed the superiority of FS-RRC in accuracy, sensitivity, specificity, stability and time complexity. Hence, integrating feature individual discriminative ability, redundancy and complementarity can define more powerful feature subset for biological data analysis, and feature complementarity can help to study the biomedical phenomena more accurately.