Statistically aided Binary Multi-Objective Grey Wolf Optimizer: a new feature selection approach for classification

Feature selection identifies the most relevant features that help improve the classifier's performance. The Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (BMOGWO-S) algorithm was recently introduced for feature selection in classification, which is a multi-objective approach to feature selection. It was identified to be providing better results than other multi-objective feature selection algorithms that are currently available. It is a metaheuristic algorithm that implements a wrapper method of feature selection. Usually, there are many statistical methods that are used for finding a relation between the input and output variables in a dataset. The BMOGWO-S algorithm does not utilize any statistical information from the dataset and relies entirely on metaheuristics. In this research, a Statistically aided Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (SaBMOGWO-S) is proposed, which uses the advantages of statistical information about the dataset's attributes. Also, methods are introduced to reduce the algorithm's running time by avoiding unnecessary computations. Results of the proposed algorithm are compared against the results obtained from the existing state-of-the-art methods with respect to 21 standard datasets from the UCI repository. For higher dimensional datasets with more than 100 features, the proposed algorithm has found to be outperforming the other methods in terms of reduction in features and classification error rate and for the lower dimensional datasets it has outperformed others in terms of run time. The best error rate obtained is 0.00 for some datasets, and the average error rate obtained for all datasets with the proposed SaBMOGWO-S is 0.11.