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Performance Evaluation of Feature Selection Measures

International Journal of Knowledge Based Computer Systems

Volume 4 Issue 2

Published: 2016
Author(s) Name: Smita Chormunge, Sudarson Jena | Author(s) Affiliation: GITAM University, Hyderabad, Telangana, India
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Abstract

Feature Selection is one of the approach for solving dimensionality problem. Numerous feature selection filter evaluation measures are used to produce good feature subset. This paper presents the comparison of Information Gain, Correlation and Gain Ratio filter measures to verify the performance of different filter evaluation measures on high dimensional datasets. Computational time required to evaluate dataset with respect to Naïve Bayes classifier is calculated by using filter measures. Experimental results on different datasets demonstrate that Correlation measure is favorable in terms of computational time than other measures.

Keywords: Feature Selection, Correlation, Information Gain, Filter Measures, Gain Ratio

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