Feature Selection via Correlation Coefficient Clustering

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/58496

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题名:	Feature Selection via Correlation Coefficient Clustering
作者:	Hsu, Hui-Huang;Hsieh, Cheng-Wei
贡献者:	淡江大學資訊工程學系
关键词:	Feature Selection;Clustering;Correlation Coefficient;Support Vector Machines (SVMs);Machine Learning;Classification
日期:	2010-12
上传时间:	2011-10-01 12:00:52 (UTC+8)
出版者:	Oulu: 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.
關聯:	Journal of Software 5(12), pp.1371-1377
DOI:	10.4304/jsw.5.12.1371-1377
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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