Binary Seagull Optimization Algorithm For Feature Selection

Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data.The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously.Searching for the(near)optimal subset of features is a challenging problem in the process of feature selection.In the literature,Swarm Intelligence algorithms show superior performance in solving this problem.This motivated our attempts to test the performance of the newly proposed Seagull Optimization Algorithm(SOA)in this area.In this paper,eight binary variants of the Seagull Optimization Algorithm(SOA)are proposed and used to select the optimal feature subset for classification purposes in a wrapper mode.SOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet.SOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function.The eight proposed binary variants of SOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features.The proposed binary algorithms are compared with seven state-of-the-art approaches.A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 12 datasets from the UCI repository.The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms,which proves the ability of SOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.