International Journal of Knowledge Based Computer Systems

2016 | Volume 4 | Issue Issue 2

Performance Evaluation of Feature Selection Measures

1. Smita Chormunge – GITAM University, Hyderabad, Telangana, India

2. Sudarson Jena – GITAM University, Hyderabad, Telangana, India

Received
- 		Accepted
- 		Published
23-Mar-2017
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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