文章基本信息

标题：CBFAST- Efficient Clustering Based Extended Fast Feature Subset Selection Algorithm for High Dimensional Data
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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：2015
卷号：4
期号：5
页码：2427-2434
出版社：Shri Pannalal Research Institute of Technolgy
摘要：The recent increase of data poses a major challenge in data extracting. High dimensional data contains high degree of irrelevant and redundant information. Feature selection is the process of eliminating such irrelevant and redundant data set with respect to the task to be performed. Several features selection techniques are used to improve the efficiency and performance of various machine learning algorithms. There are several methods that have been proposed to extract features from such high dimensional data. This paper proposes Clustering based extended Fast Feature Selection method to extract features from high dimensional data. The proposed algorithm Semi- supervised learning which is useful to partition the data in appropriate clusters. Also it selects the most frequent feature subset from the input.
关键词：Feature Selection; Search strategies; machine ; learning; clustering; relevance; semi-supervised learning