ENHANCED GREY WOLF OPTIMIZER FOR MEDICAL DATASET

Abstract

High dimensional data classification becomes challenging task because data are large, complex to handle, heterogeneous and hierarchical. 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 preprocessing 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. The proposed Hybrid kernel Improved Support Vector Machine (HISVM) classifier is used to train the parameters and optimized using Enhanced Grey wolf Optimization (EGWO). The Novel approach aimed to select minimum number of features and providing high classification accuracy.