A novel feature selection algorithm using multi-objective improved honey badger algorithm (mo-hba) and spea-ii
An important task for classification is feature selection that removes the redundant or irrelevant features from the dataset. Multi-objective feature selection approach is mainly proposed by many researches. However, these approaches fails to maintain the higher classification accuracy while removing redundancy in the features. In this work, a wrapper based feature selection technique is proposed with hybrid of Multi Objective Honey Badger Algorithm (MOHBA) and Strength Pareto Evolutionary Algorithm-II to maintain the balance between classification accuracy and removal of redundancy. Classification accuracy improvement and removal of redundant features are considered as the multi-objective optimization functions of the proposed multi-objective feature selection technique. The Levy flight algorithm is utilized to initialize the population to enhance the ability of the exploration and exploitation of MO-HBA. The regularized Extreme Learning Machine is used to classify the selected features. To evaluate the performance of the proposed feature selection technique, eighteen benchmark datasets are utilized and results are compared with the four well known multi-objective feature selection techniques in terms of accuracy, precision, recall, micro F1 score, hamming loss, ranking loss and training time. The experimental results shows that the proposed approach can give improved classification accuracy while the removal of redundancy in large scale datasets.