Quantitative Dominance-Based Neighborhood Rough Sets via Fuzzy Preference Relations

Dominance relations exist extensively in decision-making problems. Dominance-based neighborhood rough sets (DNRS) using fuzzy preference relations (FPRs) are presented in this article to deal with attribute reduction in the large-scale decision-making problems. In this model, FPR is elicited to quantify the dominance-based rough set model, which can efficiently deal with the under-fitting problem of classical dominance-based rough sets. First, by formulating a quantified dominance-based neighborhood relation which satisfies reflexivity, the propositions of the quantified DNRSs are analyzed. Second, we propose approaches to attribute reduction based on upper-approximate and lower-approximate discernibility matrices, respectively. Furthermore, we evaluate that the novel model performs efficiently and effectively in time consumption and space storage by experimental analysis. Finally, combining with parallel computing, we demonstrate that the new model can be used to deal with attribute reduction of large-scale datasets effectively.

Automated summary

This article introduces a dominance-based neighborhood rough sets model using fuzzy preference relations to deal with attribute reduction in large-scale decision-making problems. The model efficiently addresses the under-fitting issue in classical dominance-based rough sets and proposes attribute reduction approaches based on discernibility matrices. Experimental analysis confirms the model's effectiveness in time consumption and space storage, especially when combined with parallel computing for handling attribute reduction in large-scale datasets.