Three-way decisions with evaluative linguistic expressions

被引:4
|
作者
Boffa, Stefania [1 ]
Ciucci, Davide [1 ]
机构
[1] Univ Milano Bicocca, Dipartimento Informat Sistemist & Comunicaz, Viale Sarca 336, I-20126 Milan, Italy
关键词
Three-way decisions; Rough sets; Probabilistic rough sets; Evaluative linguistic expressions; Explainable Artificial Intelligence; FUZZY NATURAL LOGIC; CONFLICT; GRAMMAR;
D O I
10.1016/j.ijar.2023.109080
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The theory of three-way decisions (3WD) requires dividing a finite, non-empty universe into three disjoint sets called positive, negative, and boundary regions. Three types of decisions are then made on the objects in each region: acceptance, rejection, and abstention (or non-commitment), respectively. Until today, a large number of 3WD extensions and applications have been proposed; some of the most recent ones also include aspects of linguistics. In this article, we first propose an innovative linguistic interpretation of three-way decisions, where the positive, negative, and boundary regions are constructed by means of the so-called evaluative linguistic expressions. These are expressions of natural language, such as small, medium, very short, quite roughly strong, extremely good, etc., and they are described within a logical theory based on the formal system of higher-order fuzzy logic. Furthermore, in line with our linguistic 3WD approach, introduce the novel notion of linguistic rough sets, thus contributing to the development Rough Set Theory. Finally, we connect the theory of linguistic three-way decisions with standard 3WD model based on probabilistic rough sets, establishing conditions under which two approaches coincide. Our results highlight connections between two different research areas: three-way decisions and the theory of evaluative linguistic expressions.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Three-way Decisions Based Bayesian Network
    Gu, Yannan
    Jia, Xiuyi
    Shang, Lin
    PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATCS AND COMPUTING (IEEE PIC), 2015, : 51 - 55
  • [22] Three-way linguistic group decisions model based on cloud for medical care product investment
    Hu, Junhua
    Yang, Yao
    Chen, Xiaohong
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2017, 33 (06) : 3405 - 3417
  • [23] THREE-WAY DECISIONS WITH ARTIFICIAL NEURAL NETWORKS
    Deng, Xiaofei
    2013 26TH ANNUAL IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2013, : 762 - 765
  • [24] A Three-way Decisions Model for Decision Tables
    Yin, Linzi
    Xu, Xuemei
    Ding, Jiafeng
    Jiang, Zhaohui
    Sun, Kehui
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 258 - 263
  • [25] Decisions Tree Learning Method Based on Three-Way Decisions
    Liu, Yangyang
    Xu, Jiucheng
    Sun, Lin
    Du, Lina
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, RSFDGRC 2015, 2015, 9437 : 389 - 400
  • [26] A model of three-way decisions for Knowledge Harnessing
    Aranda-Corral, Gonzalo A.
    Borrego-Diaz, Joaquin
    Galan-Paez, Juan
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2020, 120 : 184 - 202
  • [27] Integration of Fuzzy and LSTM in Three-Way Decisions
    Subhashini, L. D. C. S.
    Li, Yuefeng
    Zhang, Jinglan
    Atukorale, Ajantha S.
    2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020), 2020, : 975 - 980
  • [28] Advances in three-way decisions and granular computing
    Fujita, Hamido
    Li, Tianrui
    Yao, Yiyu
    KNOWLEDGE-BASED SYSTEMS, 2016, 91 : 1 - 3
  • [29] An Overview of Function Based Three-Way Decisions
    Liu, Dun
    Liang, Decui
    ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014, 2014, 8818 : 812 - 823
  • [30] Three-way decisions with probabilistic rough sets
    Yao, Yiyu
    INFORMATION SCIENCES, 2010, 180 (03) : 341 - 353