A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection

Datasets used in data analysis often contain irrelevant or redundant attributes. These attributes hinder the performance of predictive models. Therefore, an effective preprocessing feature selection procedure is essential to identify the relevant features and eliminate unnecessary ones. Metaheuristic algorithms, inspired mainly by nature, are strong candidates for the feature selection process, as they can efficiently search large solution spaces. Metaheuristic algorithms provide flexible strategies for complex optimization problems such as traveling salesman problem, scheduling problems, nonlinear integer programming, and multi-objective optimization. Reasonably balancing exploration and exploitation will…