文章基本信息

标题：Feature Selection for High Dimensional and Imbalanced Data- A Comparative Study
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作者：Kokane Vina A. ; Lomte Archana C.
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：11
页码：3800-3804
出版社：Shri Pannalal Research Institute of Technolgy
摘要：The recent increase of data poses a severe challenge in data extracting. High dimensional data can contain high degree of irrelevant and redundant information. Feature selection is the process of eliminating irrelevant data set with respect to the task to be performed. Several features selection techniques are used to improve the efficiency of various machine learning algorith ms. There are several methods that have been proposed to extract features from such high dimensional data. This paper proposes a study of various methods for feature selection and it has been found that Clustering based Feature selection methods are most effective in selecting important features.
关键词：Feature selection; Search strategies; Machine ; learning; Clustering; Relevance