文章基本信息

标题：Efficient Attribute Evaluation, Extraction and Selection Techniques for Data Classification
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作者：Gaurang Panchal ; Devyani Panchal
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：2
页码：1828-1831
出版社：TechScience Publications
摘要：In machine learning and statistics, feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models. Feature selection also helps people to acquire better understanding about their data by telling them which are the important features and how they are related with each other. Attribute subset selection on the basis of relevance analysis is one way to reduce the dimensionality. Relevance analysis of attribute is done by means of correlation analysis, which detects the attributes (redundant) that do not have significant contribution in the characteristics of whole data of concern. Feature selection is one of the important and frequently used techniques in data preprocessing for data mining. It reduces the number of features, removes irrelevant, redundant, or noisy data, and brings the immediate effects for applications. This paper shows various feature selection techniques for various dataset. We have taken Intrusion Detection Problem Dataset and Gas Consumption Dataset for testing. The comparison of various feature selection techniques discussed in this paper
关键词：Crossover; Genetic Algorithm; Mutation; Random;Population