文章基本信息

标题：Efficient Feature Subset Selection using Kruskal's Process in Big Data
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作者：S.Saranya ; S.L.Julian Austrina ; K.Ravikumar 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：4
DOI：10.15680/ijircce.2015.0304171
出版社：S&S Publications
摘要：Feature selection involves identifying a subset of the most useful features that produces compatibleresults as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency andeffectiveness points of view. While the efficiency concerns the time required to find a subset of features, theeffectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based featureselection algorithm, FAST, is proposed and experimentally evaluated. The FAST algorithm works in two steps. In thefirst step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the mostrepresentative feature that is strongly related to target classes is selected from each cluster to form a subset of features.
关键词：Feature subset selection; filter method; feature selection; graph-based clustering