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Abstract
Feature selection Genetic algorithm has become an essential task in data mining for various real-world problems. This method is used to remove the redundant and irrelevant features for increasing the accuracy and prediction in classification. This selection method is also used for computing performance, efficiency and interpretability by using supervised and unsupervised learning methods. In this survey paper overview of recent advances in feature selection and classification is illustrated for various domains. In addition to feature selection filter, wrapper and embedded methods are the recent approaches for selecting subsets efficiently with hybrid method. The advantages and disadvantages are outlined to provide clear insight for computational memory and best accuracy.