A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III

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In solving two or more objective problems, multi-objective evolutionary algorithms (MOEAs) have proven their effective performance. In most of the MOEAs based feature selection algorithms, more optimal solutions are obtained around the Pareto front's center because of the deficiency in selection features. With the complicated Pareto fronts, it is difficult to select features using the penalty boundary intersection decomposition approach that can provide stable selection pressures. With the adaptive penalty boundary intersection (APBI) decomposition approach, a novel wrapper based feature selection technique named MOGHBNS3/D is proposed in this paper based on the hybrid of Multi Objective Guided Honey Badger Algorithm and Non-dominated sorting genetic algorithm III. Classification accuracy improvement and removal of redundant features are considered to be the multi-objective optimization functions of the proposed multi-objective feature selection technique. An external archive is employed as a repository for storing the non-dominated solutions in the MOGHBNS3/D. To enhance the selection pressures of the external archive, the penalty values are adjusted adaptively using the APBI mechanism. The Wavelet kernel Extreme Learning Machine is used to classify the selected features. To analyze the performance of the proposed feature selection technique, eighteen benchmark datasets are utilized and results are compared with the four well known multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage error, hamming loss, ranking loss and training time. The experimental results show that the proposed method can select optimal features with improved classification accuracy, diversity and convergence while the removal of redundancy.