A Hybridization Approach for Optimal Feature Subset Selection in High Dimensional Data

Abstract

Feature subset selection assumes an essential part in the fields of data mining and machine learning. A good feature subset selection algorithm can adequately expel unimportant and repetitive elements and consider feature interaction. This not just paves the way to an understanding comprehension of the information, additionally enhances the execution of a learner by improving the generalization capacity and the interpretability of the learning model. Initially, the input micro array dataset is selected from the medical database. Then preprocessing step is done in the input micro array dataset. The resultant output is fed to the second step; here the features are optimally selected using clustering and optimization process. In our proposed technique, the optimal hybrid fuzzy c-means clustering algorithm with artificial bee colony algorithm is applied on the high dimensional micro array dataset to select the important features. Here the proposed method is optimally select the features with the help of social spider optimization algorithm. After that, the classification is done through improved support vector machine classifier. At last, the experimentation is performed by means of different micro array dataset. Experimental results indicate that the proposed classification framework has outperformed by having better accuracy of 93.19% for GLA-BRA-180 dataset when compared existing SVM and neuro fuzzy classifier only achieved 90.69% and 89%.