An Optimization of Feature Selection for Classification Using Modified Bat Algorithm

Abstract

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role nowadays in all sorts of fields like Medical, Engineering, Banking, Education, and Fraud detection. In this paper, Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search, and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We proposed two different techniques from Wrapper-based feature selection methods namely Forward/Backward Elimination and Exhaustive Search to find best optimal solution to perform classification using different Nature-Inspired Algorithms out of which Modified Bat Algorithm is proposed in this paper. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on UCI datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work implemented using JAVA and the Medical dataset (UCI) has been used. These datasets were chosen due to nominal class features. The number of attributes, instances, and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly.