A new feature selection method based on symmetrical uncertainty and interaction gain.

Defining important information from complex biological data is of great significance in biological study. It is known that the physiological and pathological changes in an organism are usually influenced by molecule interactions. Analyzing biological data by fusing the evaluation of the individual molecules and molecule interactions could induce a more accurate and comprehensive understanding of the organism. This study proposes an Interaction Gain - Recursive Feature Elimination (IG-RFE) method which evaluates the feature importance by combining the relevance between feature and class label and the interaction among features. Symmetrical uncertainty is adopted to measure the relevance between feature and the class label. The average normalized interaction gain of feature f, every other features and the class label is calculated to reflect the interaction of feature f with other features in the feature set F. Based on the combination of symmetrical uncertainty and normalized interaction gain, less important features are removed iteratively. To show the performance of IG-RFE, it was compared with seven efficient feature selection methods, MIFS, mRMR, CMIM, ReliefF, FCBF, PGVNS and SVM-RFE, on eleven public datasets. The experiment results showed the superiority of IG-RFE in accuracy, sensitivity, specificity and stability. Hence, integrating feature individual discriminative ability and the interaction among features could better evaluate feature importance in biological data analysis.