A novel ensemble-based wrapper method for feature selection using extreme learning machine and genetic algorithm

This paper presents a novel wrapper feature selection algorithm for classification problems, namely hybrid genetic algorithm (GA)- and extreme learning machine (ELM)-based feature selection algorithm (HGEFS). It utilizes GA to wrap ELM to search for the optimum subsets in the huge feature space, and then, a set of subsets are selected to make ensemble to improve the final prediction accuracy. To prevent GA from being trapped in the local optimum, we propose a novel and efficient mechanism specifically designed for feature selection problems to maintain GA's diversity. To measure each subset's quality fairly and efficiently, we adopt a modified ELM called error-minimized extreme learning machine (EM-ELM) which automatically determines an appropriate network architecture for each feature subsets. Moreover, EM-ELM has good generalization ability and extreme learning speed which allows us to perform wrapper feature selection processes in an affordable time. In other words, we simultaneously optimize feature subset and classifiers' parameters. After finishing the search process of GA, to further promote the prediction accuracy and get a stable result, we select a set of EM-ELMs from the obtained population to make the final ensemble according to a specific ranking and selecting strategy. To verify the performance of HGEFS, empirical comparisons are carried out on different feature selection methods and HGEFS with benchmark datasets. The results reveal that HGEFS is a useful method for feature selection problems and always outperforms other algorithms in comparison.