Department of Computer Science and Information Engineering, Tamkang University, Taipei, 25137, Taiwan

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Abstract—Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values. The most class-dependent feature in each cluster is retained while others in the same cluster are removed. Thus, the most class-related and mutually unrelated features are identified. The proposed method was applied to two datasets: the disordered protein dataset and the Arrhythmia (ARR) dataset. The experimental results show that the method is superior to other feature selection methods in speed and/or accuracy. Detail discussions are given in the paper.

Index Terms—Feature Selection, Clustering, Correlation Coefficient, Support Vector Machines (SVMs), Machine Learning, Classification.

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Cite: Hui-Huang Hsu, Cheng-Wei Hsieh, "Feature Selection via Correlation Coefficient Clustering," Journal of Software vol. 5, no. 12, pp. 1371-1377, 2010.