A novel extension method of vpfrs mode for attribute reduction problem in numerical decision tables - Ngoc Ha, Pham Minh; Dai, Tran Thanh; Hung, Nguyen Manh; Dung, Hoang Tuan (Journal of Computer Science and Cybernetics, 2024)
Attribute reduction is an essential application of the Rough Set (RS) theory that has been receiving the attention of many researchers. Up to now, attribute reduction methods to improve classification accuracy on noisy datasets following the IFRS approach still have many limitations in terms of computation time. In this paper, we use the Variable Precision (VP) method on approximate Operators of the FRS model to construct the VPOFRS model. The main contributions of this paper include: 1) proposing the novel model based on the Variable Precision for approximate Operators of Fuzzy Rough Set (VPOFRS) model, 2) proposing some measures to evaluate the consistency degree of the decision table and the dependence degree of the attribute, 3) proposing an attribute reduction algorithm VPOFRS_AR. Experimental results on noisy datasets from UCI show that the proposed method not only improves the noise for the reduct but the algorithm’s execution time is faster than other algorithms.