A new hybrid feature selection based on Improved Equilibrium Optimization

Feature selection is one of the most important preprocessing steps for analyzing high-dimensional data in the data mining and machine learning fields. In machine learning problems, Classification accuracy is highly dependent on the selected features of a dataset. The main purpose of feature selection is to remove additional and irrelevant features from the data feature set to increase the performance of algorithms. A novel feature selection model is proposed in this paper. In the first step, the features are scored based on the mRMR feature selection approach, and those with a higher score are selected. In the second step, the Wrapper feature selection approach is used to extract the best features based on the Improved Equilibrium Optimization (IMEO) algorithm. IMEO uses a new operator called Entropy-based to improve the performance of the Equilibrium Optimizer (EO) algorithm and prevents the algorithm from getting trap in local optima by controlling the exploration and exploitation abilities. Also, Levy flight is used to find new solutions in the search space and to improve the exploration phase of the EO algorithm. This mechanism will avoid local optimum by creating many short jumps and sometimes large jumps in the search space. The proposed IMEO algorithm is first tested on 23 benchmark test functions; after ensuring the efficiency of the proposed method, the Binary Improved Equilibrium Optimization (BIMEO) algorithm is defined based on the sigmoid transfer function to solve the feature selection problem. The simulation results of the BIMEO algorithm and state-of-the-art algorithms on 18 standard benchmark datasets show the performance of the proposed model compared to the other algorithms. In addition, the results of experiments on two chemical datasets prove the efficiency of the proposed algorithm, BIMEO, in terms of selective features and classification accuracy.