Robust Attribute Reduction Exploring Class-Separability and Attribute-Correlation for Ordered Decision Systems

Abstract

Robust knowledge acquisition approaches are one of the research hotspots in data mining. The fuzzy dominance rough sets (FDRS) model is an important knowledge acquisition tool for ordinal classification tasks. However, it has been proved in practice that this model usually performs poorly fault tolerance, and only one noisy sample can cause huge interference in acquiring knowledge. Attribute reduction is one of the nontrivial applications of the FDRS. At present, most attribute reduction methods for ordinal classification tasks mainly focus on the dependence of decision to attributes, while ignoring the information provided by the separability of classes and the correlation of attributes for ordinal classification. In view of these two issues, this article first proposes a robust FDRS model with filterable noise samples. Then, the class-separability and attribute-correlation are explored in robust fuzzy dominance rough approximation space, and corresponding attribute evaluation index is designed. Finally, an attribute reduction algorithm is designed to select the attribute subset with the highest classification performance. The experimental results show that the proposed algorithm has better robustness and classification performance.