Fuzzy Rough Set Feature Selection Based on Inconsistent Nearest Neighbors
Fuzzy rough sets can break the limitation of classical rough sets that can only handle discrete data,effectively selecting features for continuous numerical values.However,they are object-centered and have high time complexity,rendering the handling of high-dimensional and large-scale data difficult.An inconsistent nearest neighbor acceleration strategy is proposed based on the horizontal cut set.This strategy tracks the fuzzy nearest neighbor set of each object in the domain,continuously pruning the nearest neighbors that do not affect the calculation.The object is pruned if the inconsistent nearest neighbors of the object are completely pruned,improving algorithm efficiency.At the same time,designing an attribute importance reduction based on inconsistent nearest neighbors can effectively suppress redundant feature selection,improving efficiency and classification accuracy.The proposed acceleration strategy and attribute importance do not affect the attribute selection's order.On this basis,a new fuzzy rough set feature selection algorithm is proposed.The experimental results on 9 UCI and scikit datasets show that the algorithm not only effectively reducing runtime but also achieving high classification accuracy.Compared with the FA-FSCE,AVDP,and IV-FS-FRS-2 algorithms,the running time of this algorithm can be reduced by at least 9.44%,especially on high-dimensional and large-scale datasets by 61.01%to 99.54%.The classification accuracy of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)can be improved by up to 11.20%and 19.95%,respectively.
fuzzy rough setfeature selectionlevel-setinconsistent nearest neighborssignificance of attributes
国家科技期刊平台
NSTL
万方数据
维普
