Extension of labeled multiple attribute decision making based on fuzzy neighborhood three-way decision

Abstract

Weight assignment of attribute is considered as a key part of multiple attribute decision making (MADM), and this is also applicable to labeled multiple attribute decision making (LMADM) that is a decision theory specially proposed for the dataset with labels. However, regarding the decision making of massive data characterized by redundancy and uncertainty, more means including attribute selection and uncertainty processing should be considered to solve these problems. Based on the traditional framework of LMADM, this paper deduces a new framework to adapt to the decision making of massive data. With respect to the uncertainty generated from data and decision process, a fuzzy neighborhood three-way decision model (FN3WD) is proposed, in which the fuzzy neighborhood relationship can address the uncertainty of data and the three-way decision theory can deal with the uncertainty of decision process. Finally, the experimental results illustrate the superiority of FN3WD and verify the effectiveness of the proposed framework of the extended LMADM by using some benchmarked datasets and the Commercial Modular Aero-Propulsion System Simulation dataset.