[1]XU Jianfeng,WANG Shuyi,YAO Yiyu,et al.Visualization concepts and analysis of three-way decisions[J].CAAI Transactions on Intelligent Systems,2026,21(2):542-552.[doi:10.11992/tis.202507014]
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Visualization concepts and analysis of three-way decisions

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 542-552 Column: 学术论文—人工智能基础 Public date: 2026-03-05
Title:
Visualization concepts and analysis of three-way decisions
Author(s):
XU Jianfeng1; 2; 3;  WANG Shuyi2;  YAO Yiyu3;  MIAO Duoqian4
1. School of Mathermatics and Computer Sciences, Nanchang University, Nanchang 330031, China;
2. School of Software, Nanchang University, Nanchang 330047, China;
3. Department of Computer Science, University of Regina, Regina S4S 0A2, Canada;
4. School of Computer Science and Technology, Tongji University, Shanghai 201804, China
Keywords:
three-way decisions; decision-theoretic rough sets; Bayesian risk theory; three-way concept analysis; visualization; probabilistic rough sets; double-quantitative; geometric interpretation
CLC:
TP182
DOI:
10.11992/tis.202507014
Abstract:
Three-way decision is a novel theory for solving uncertain problems, and enhancing its interpretability is a key research direction. While visualization is a powerful means of achieving interpretability in computer science, a systematic theoretical framework for visualizing three-way decisions has not yet been established, creating an urgent need for highly interpretable visualization tools. Therefore, based on the classic models of three-way decisions, this paper proposes a set of methods for visual modeling and reasoning. Firstly, building on a visual representation of the fundamental concepts, this paper addresses the visualization of Decision-Theoretic Rough Sets (DTRS) by constructing a Probability-Cost visualization space. Within this space, the geometric semantics and monotonicity of the three cost-objective functions are visually interpreted, which in turn enables a visual derivation of the decision thresholds. Secondly, extending this work to the visualization of Double-Quantitative Three-Way Decisions (DQ-TWD), we further construct a two-dimensional Relative Conditional Probability-Absolute Conditional Probability space for the visual analysis of the nine double-quantitative decision types. Finally, a case study using a mushroom dataset verifies that the proposed visualization tools provide effective and interpretable decision support for complex uncertain reasoning applications, demonstrating strong potential for generalization and practical application.
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