Feature selection for classification with class-separability strategy and data envelopment analysis

In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handled on each class label. Super-efficiency DEA is employed to evaluate and rank features via their conditional dependence scores on all class labels, and the feature with maximum super-efficiency score is then added in the conditioning set for conditional dependence estimation in the next iteration, in such a way as to iteratively select features and get the final selected features. Eventually, experiments are conducted to evaluate the effectiveness of proposed method compared with five state-of-the-art methods from the viewpoint of classification accuracy. Empirical results verify the feasibility and the superiority of proposed feature selection method. HighlightsFeature selection is taken as a DEA-based evaluation process.Class-separability strategy is applied to explicitly handle relevance and redundancy on each class label.Super-efficiency DEA is employed to evaluate features via their conditional dependence scores on all class labels.