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An active area of research in data mining and machine learning is dimensionality reduction. Feature subset selection is an effective technique for dimensionality reduction and an essential step in successful data mining applications. It reduces the number of features, removes irrelevant, redundant, or noisy features, and enhances the predictive capability of the classifier. It provides fast and cost-effective predictors and leading to better model comprehensibility. In this paper, we proposed adoi:10.14569/ijacsa.2018.090142 fatcat:armvbgepuzhpznhzee2r5oufxu