Feature selection method based on neighborhood rough sets and marine predator algorithm

he feature selection method in rough set model has large computational overhead and can't directly handle continuous data, and Marine Predator Algorithm (MPA) still has some problems, such as slower convergence speed and easy to fall into local optimum. Therefore, a feature selection method based on neighborhood rough set (NRS) and Marine Predator Algorithm is proposed. Firstly, the original algorithm is improved by using opposition-based learning based on Tent Chaotic Map and Gaussian Perturbation strategy to obtain IMPA, and then a transmission mechanism is constructed to form a binary algorithm. Then, a fitness function is developed based on the neighborhood dependence in NRS and the length of feature subset. IMPA is used to iteratively search for the optimal feature subset, and a meta-heuristic feature selection algorithm is designed. Finally, the optimization performance of IMPA on 9 benchmark functions and the classification ability of feature selection algorithm on UCI data set is evaluated. Experimental results show IMPA is significantly better than Particle Swarm Optimization (PSO) algorithm and Salp Swarm Algorithm (SSA) in terms of average value and standard deviation. On UCI datasets, compared with the optimized feature selection algorithms based on rough sets and the optimized feature selection algorithms based on neighborhood rough sets, the average values of classification accuracy of the proposed feature selection method using KNN classifier is improved by 10.28~14.13 percentage points, 2.71~12.11 percentage points respectively. The average values of classification accuracy of the proposed feature selection method using CART classifier is improved by 9.41~13.24 percentage points, 2.90~12.31 percentage points respectively.