An efficient and interactive feature selection approach based on copula entropy for high-dimensional genetic data

Feature selection (FS) is especially important for high-dimensional data. In this paper, we propose an efficient and interactive feature selection approach based on copula entropy (CEFS+). The method combines feature-feature mutual information with feature-label mutual information and uses a maximum correlation minimum redundancy strategy for greedy selection. The approach uses copula entropy as a measure of feature relevance that captures the full-order interaction gain between features. Moreover, we prove the divisibility of multivariate mutual information, and derive a novel feature criterion, and propose a feature selection approach based on copula entropy called CEFS. Meanwhile, to overcome the instability of the CEFS method on some datasets, we propose the improved method CEFS+ which based on the rank technique. Finally, we evaluate the effectiveness of CEFS and CEFS+ using three classifiers on five datasets. In 10 out of 15 scenarios, our approach obtains the highest classification accuracy, which is much higher than the other six commonly used FS methods. In particular, our approach performs better on high-dimensional genetic datasets.