Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method

Abstract

<jats:p>Three-way decisions, as a general model for uncertain information processing and decisions, mainly utilize the threshold generated by the decision cost matrix to determine the decision category of the object. However, the determination of the threshold is usually accompanied by varying degrees of subjectivity. In addition, the potential symmetrical relationship between the advantages and disadvantages of the decision cost is also a problem worthy of attention. In this study, we propose a novel intuitionistic fuzzy three-way decision (IFTWD) model based on a three-way granular computing method. First, we present the calculation methods for the possibility of membership state and non-membership state, as well as prove the related properties. Furthermore, we investigate the object information granules, i.e., the fine-grained, medium-grained, and coarse-grained objects, by combining the state probability distribution and probability distribution. Then, for decision and evaluation issues, we define the superiority-compatibility relation and inferiority-compatibility relation for IFTWD model construction. In addition, we use the superiority degree and inferiority degree instead of the original thresholds and design a new method for evaluating decision cost. Finally, we focus on the algorithm research of the proposed model and present an empirical study of agricultural ecological investment in Hubei Province to demonstrate the effectiveness of our model.</jats:p>

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

“…3WD theory has been widely used in many areas, such as emerging three-way formal concept analysis [ 54 ], three-way conflict analysis [ 55 ], three-way granular computing [ 56 ], three-way classification [ 57 ], three-way recommendation [ 58 ], and three-way clustering [ 59 ]. This paper will combine the idea of 3WD to improve the performance of heterogeneous ASD data diagnosis by reducing the difference in the data distributions between the source domain and target domain.…”