Feature selection using data envelopment analysis

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Feature selection has been attracting increasing attention in recent years for its advantages in improving the predictive efficiency and reducing the cost of feature acquisition. In this paper, we regard feature selection as an efficiency evaluation process with multiple evaluation indices, and propose a novel feature selection framework based on Data Envelopment Analysis (DEA). The most significant advantages of this framework are that it can make a trade-off among several feature properties or evaluation criteria and evaluate features from a perspective of “efficient frontier” without parameter setting. We then propose a simple feature selection method based on the framework to effectively search “efficient” features with high class-relevance and low conditional independence. Super-efficiency DEA is employed in our method to fully rank features according to their efficiency scores. Experimental results for twelve well-known datasets indicate that proposed method is effective and outperforms several representative feature selection methods in most cases. The results also show the feasibility of proposed DEA-based feature selection framework.