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In today's world, machine learning has become a vital part of our lives. When applied to real-world applications, machine learning encounters the difficulty of high dimensional data. Unnecessary and redundant features can be found in data. The performance of classification algorithms employed in prediction is harmed by these superfluous features. The primary step in developing any decision support system is to identify critical features. In this paper, the authors have proposed a hybrid feature selection method CFGA by integrating CFS (correlation feature selection) and GA (genetic algorithm). The efficiency of the proposed method is analyzed using logistic regression classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. The proposed CFGA method is also compared to six other feature selection methods. Results demonstrate that proposed method has increased the performance of the classification system by removing irrelevant and redundant features.

A Hybrid Approach for Feature Selection Based on Correlation Feature Selection and Genetic Algorithm