BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

The increased utilization of digital instruments like smartphones, Internet of Things (IoT) sensors, cameras, and microphones has resulted in extensive amounts of big data. Inherent challenges associated with big data include significant data dimensionality, redundancy, and irrelevant information. The main objective of feature selection is eliminating unnecessary features, thereby minimizing time and space requirements. This paper proposes a new Binary Crayfish Optimization Algorithm (BinCOA) for feature selection. The Crayfish Optimization Algorithm (COA) is a new metaheuristic algorithm inspired by the simulation of Crayfish search for food, summer resorts, and competitive habits. The original COA has been augmented with two primary enhancements to improve its performance. The refracted opposition-based learning strategy is a novel enhancement incorporated into the initialization step of the COA algorithm to strengthen the algorithm’s capability for exploitation. The crisscross strategy is added to the original COA, increasing the COA’s convergence accuracy. The algorithm’s performance is assessed by evaluating a set of 30 benchmark datasets. The proposed BinCOA is evaluated in comparison to seven contemporary wrapper feature selection methods. The experimental finding indicates that BinCOA consistently outperforms existing algorithms in classification accuracy, average fitness value, and the number of selected features. Furthermore, the statistical significance of the results is verified by calculating the Wilcoxon rank-sum test.