Efficient Feature Selection and Classification Technique For Large Data

Grey wolf optimizer (GWO) is a Heuristic evolutionary algorithm recently proposed, it is inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. In order to reduce the data set without affecting the classifier accuracy. The feature selection plays a vital role in large datasets and which increases the efficiency of classification to choose the important features for high dimensional classification, when those features are irrelevant or correlated. Therefore, feature selection is considered to use in pre-processing before applying classifier to a data set. Thus, this good choice of feature selection leads to the high classification accuracy and minimize computational cost. Though different kinds of feature selection methods are investigate for selecting and fitting features, the best algorithm should be preferred to maximize the accuracy of the classification. This paper proposes intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. In this paper, initial subset selection is based on the latest bio inspired Grey wolf optimization technique proposed. Which take off the hunting process of gray wolve. This optimizer search the feature space for optimal feature solution in diverse directions in order to minimize the option of trapped in local minimum and enhance the convergence speed. The Novel approach aimed to speed up the training time and optimize the SVM classifier accuracy automatically. The proposed model used to select minimum number of features and providing high classification accuracy of large datasets.