文章基本信息

标题：Feature Selection via Correlation Coefficient Clustering
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作者：Hsu, Hui-Huang ; Hsieh, Cheng-Wei
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2010
卷号：5
期号：12
页码：1371-1377
DOI：10.4304/jsw.5.12.1371-1377
语种：English
出版社：Academy Publisher
摘要：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.
关键词：Feature Selection; Clustering; Correlation Coefficient; Support Vector Machines (SVMs); Machine Learning; Classification