Feature Selection Based on Adaptive Whale Optimization Algorithm and Fault-Tolerance Neighborhood Rough Sets

Traditional whale optimization algorithm(WOA) cannot handle continuous data effectively, and the tolerance of neighborhood rough sets(NRS) for noise data is poor. To address the issues, an algorithm of feature selection based on adaptive WOA and fault-tolerance NRS is presented. Firstly, a piecewise dynamic inertia weight based on iteration cycle is proposed to prevent the WOA from falling into local optimum prematurely. The shrinkage enveloping and spiral predation behaviors of WOA are improved, and an adaptive WOA is designed. Secondly, the ratio of the same decision features in the neighborhood is introduced to make up for the fault tolerance lack of NRS model for noise data, and the upper and lower approximations, approximation precision and approximation roughness, fault-tolerance dependence and approximation conditional entropy of fault-tolerance neighborhood are defined. Finally, a fitness function is constructed based on the fault-tolerance NRS, and then the adaptive WOA searches for the optimal feature subset through continuous iterations. The Fisher score is employed to reduce the dimensions of high-dimensional datasets preliminarily and the time complexity of the proposed algorithm effectively. The proposed algorithm is tested on 8 low-dimensional UCI datasets and 6 high-dimensional gene datasets. Experimental results demonstrate that the proposed algorithm selects fewer features effectively with high classification accuracy. © 2022, Science Press. All right reserved.