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

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

Automated summary

This paper proposes an advanced method for forming three-way decision classification rules, which uses information granules and information entropy to describe uncertainty and form fuzzy rules to solve classification problems. Experimental results show that classification rules considering uncertain data perform better in decision-making processes and have an improvement compared to traditional methods.