Feature selection based on self-information combining double-quantitative class weights and three-order approximation accuracies in neighborhood rough sets

Abstract

Feature selection is related to information processing, and its measurement and algorithm use various intelligent methodologies, such as neighborhood rough sets (NRSs). At present by NRSs, the relative neighborhood self-information (Relative-NSI) introduces an information function for algebraic characterization, and its feature selection algorithm (NSI-FS) has been successfully applied. However, Relative-NSI and NSI-FS ignore the underlying recognition information of decision classes; thus, they have the advancement space. In this study, absolute rates and correlative coefficients of decision classes are double-quantitatively introduced, and three-order approximation accuracies are systematically established to induce a robust self-information measure; thus, corresponding feature selection optimally improves NSI-FS to have generalization abilities. Firstly, three-order approximation accuracies are constructed by using two-time class weights of absolute rates and Spearman's correlation coefficients, and the new measure called ClaWNSI promotes Relative-NSI in terms of class recognition. Then, the three-order approximation accuracies and subsequent ClaWNSI are evolved in matrix forms, and relevant class weights of Spearman's correlation coefficients are realized by two vectors from approximation matrices. Furthermore, ClaWNSI and its feature significance motivate a heuristic selection algorithm (called ClaWNSI-FS). Finally, data experiments on 14 datasets validate ClaWNSI and ClaWNSI-FS; ClaWNSI-FS outperforms NSI-FS and five other contrast algorithms to acquire better classification performance.