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

TL;DR

This paper introduces a novel feature selection method combining class-separability strategy and Data Envelopment Analysis, which improves classification accuracy by explicitly handling relevance and redundancy for each class label.

Contribution

The paper proposes a new feature selection approach that uses super-efficiency DEA and class-separability to better evaluate feature relevance and redundancy for multi-class classification.

Findings

The proposed method outperforms four state-of-the-art methods in classification accuracy.

Experimental results demonstrate the effectiveness and superiority of the new feature selection approach.

The method effectively captures the relationship between features and class labels through class-separability.

Abstract

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 comparing with four state-of-the-art methods from the viewpoint of classification accuracy. Empirical results verify the feasibility and the superiority of proposed feature selection method.