Feature selection by maximizing correlation information for integrated high-dimensional protein data

This paper primarily proposes a novel method, named maximum correlation information (MCI), to evaluate the importance of each feature by maximizing correlation information between feature space and class coding space. Then the proposed MCI is combined with the strategy of recursive feature elimination to form a new feature selection method, MCI-based recursive feature elimination (MCI-RFE). MCI-RFE aims to select the optimal features with lower time complexity. To validate the performance of the proposed method, random 10-fold cross-validation is applied thirty times on six widely used benchmark datasets with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). The experimental results show that MCI-RFE is highly competitive and works effectively for integrated high-dimensional protein data compared to the other state-of-the-art algorithms including SVM-RFE, ReliefF-RFE and Random-Forest.