Granular-Ball Three-Way Decision

Xin Yang, Yanhua Li, Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Tianrui Li

Abstract: By thinking, information processing and decision-making in threes, the idea, theory and methods of three-way decision have been successfully applied to various domains. However, the current three-way decision has two following limitations. On the one hand, the narrow three-way decision associated with rough sets either has trouble processing continuous data or fails to represent knowledge by equivalence classes. On the other hand, the inputs of generalized three-way decision are individual objects rather than equivalence classes, which reduces the decision efficiency. To this end, we try to integrate efficient granular-ball computing into three-way decision. Firstly, we propose a novel model, i.e., granular-ball three-way decision to improve the efficiency and robustness of three-way decision. Secondly, sequential three-way decision based on granular-ball is presented to investigate the appropriate multi-granularity structures and represent the same object at different granularities. Finally, we analyze the advantages of granular-balls to strengthen the real-world applications of three-way decision.