Feature Selection With Fuzzy-Rough Minimum Classification Error Criterion

Classical fuzzy rough set often uses fuzzy rough dependency as an evaluation function of feature selection. However, this function only retains the maximum membership degree of a sample to one decision class, it cannot describe the classification error. Therefore, in this article, a novel criterion function for feature selection is proposed to overcome this weakness. To characterize the classification error rate, we first introduce a class of irreflexive and symmetric fuzzy binary relations to redefine the concepts of fuzzy rough approximations. Then, we propose a novel concept of dependency: inner product dependency to describe the classification error and construct a criterion function to evaluate the importance of candidate features. The proposed criterion function not only can maintain a maximum dependency function, but also guarantees the minimum classification error. The experimental analysis shows that the proposed criterion function is effective for datasets with a large overlap between different categories.

Automated summary

This article proposes a novel criterion function for feature selection by redefining the concepts of fuzzy rough approximations using a class of irreflexive and symmetric fuzzy binary relations, and introducing the concept of inner product dependency to describe classification errors. Experimental results demonstrate the effectiveness of the proposed criterion function for datasets with a large overlap between different categories.