ECM-EFS: An ensemble feature selection based on enhanced co-association matrix

Currently, feature selection faces a huge challenge that no single feature selection method can effectively deal with various data sets for all real cases. Ensemble learning is a potential promising solution to address this problem. We propose an ensemble feature selection method based on enhanced co-association matrix (ECM-EFS). Positive-co-association matrix (PCM), negative-co-association matrix (NCM), and relative-co-association matrix (RCM) are first introduced to discover the relationship among features by ensembling the results in multiple feature selection methods. To further produce a more stable feature selection result, "Feature Kernel" is also introduced and used as a starting point for feature selection. Comparative experiments with four state-of-the-art methods have confirmed that the ECM-EFS can provide more robust results. Moreover, compared with traditional ensemble feature selection methods, our method can compensate information loss and reduce computational cost significantly. (c) 2023 Elsevier Ltd. All rights reserved.