Wrapper Framework for Test-Cost-Sensitive Feature Selection

Feature selection is an optional preprocessing procedure and is frequently used to improve the classification accuracy of a machine learning algorithm by removing irrelevant and/or redundant features. However, in many real-world applications, the test cost is also required for making optimal decisions, in addition to the classification accuracy. To the best of our knowledge, thus far, few studies have been conducted on test-cost-sensitive feature selection (TCSFS). In TCSFS, the objectives are twofold: 1) to improve the classification accuracy and 2) to decrease the test cost. Therefore, in fact, it constitutes a multiobjective optimization problem. In this paper, we transformed this multiobjective optimization problem into a single-objective optimization problem by utilizing a new evaluation function and in this paper, we propose a new general wrapper framework for TCSFS. Specifically, in our proposed framework, we add a new term to the evaluation function of a wrapper feature selection method so that the test cost of measuring features is taken into account. We experimentally tested our proposed framework, using 36 classification problems from the University of California at Irvine (UCI) repository, and compared it to some other state-of-the-art feature selection frameworks. The experimental results showed that our framework allows users to select an optimal feature subset with the minimal test cost, while simultaneously maintaining a high classification accuracy.