Granular Description of Uncertain Data for Classification Rules in Three-Way Decision

Considering that data quality and model confidence bring threats to the of decision-making, a three-way decision with uncertain description is more meaningful in system analyses. In this paper, an advanced method for forming classification rules decisions proposed. This firstly constructs information granules describing decision-making; meanwhile, entropy introduced Granular Computing (GrC) realize better uncertainty description. Then, based on constructed descriptors, fuzzy are formed aiming at common decision-making processes, namely problems. Finally, experiments both synthetic publicly available implemented. Discussions numerical results validate feasibility proposed rules. Moreover, consideration demonstrated be performed than traditional methods improvement 1.35–4.26% processes.