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

Abstract

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.