Three-Way Decision Model based on Probabilistic Dominance Relations under Interval-Valued Intuitionistic Fuzzy Number

Abstract

Multi-attribute decision-making (MADM) is crucial in various fields. Most of MADM methods based on attribute values primarily handle scenarios where attribute information is expressed as an exact value. With the wide application of methods, the problem of uncertainty arises gradually, leading to attribute values that are not single values. The paper constructs a three-way decision (TWD) model based on probabilistic dominance relation (PDR) under interval-valued intuitionistic fuzzy number (IVIFN). This model is designed to address imprecise information by utilizing IVIFN. Meanwhile, we innovatively introduce attribute dominance degree to construct loss function matrix, which makes full use of interval number information. Moreover, we propose a novel method to calculate conditional probability under PDR, thus constructing a novel TWD model that assists decision makers in ranking and classifying. Finally, this paper demonstrates the reliability of the model in ranking through Spearman rank correlation coefficient (SRCC). Furthermore, the experimental result on UCI dataset shows that the model has great classification capabilities, with a 3.1% reduction in the average error rate.