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《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (3): 41-48.doi: 10.6040/j.issn.1671-9352.4.2021.010

• • 上一篇    

基于TOPSIS的无标签序贯三支决策模型

杨洁1,2*,罗天1,李阳军1   

  1. 1. 遵义师范学院物理与电子科学学院, 贵州 遵义 563002;2. 重庆邮电大学计算智能重庆市重点实验室, 重庆 400065
  • 发布日期:2022-03-15
  • 作者简介:杨洁(1987— ),男,副教授,博士,研究方向为粒计算、数据挖掘、粗糙集. E-mail:yj530966074@foxmail.com*通信作者
  • 基金资助:
    国家自然科学基金资助项目(62066049);贵州省科技计划项目(黔科合基础-ZK［2021一般332］;贵州省教育厅青年科技人才成长项目(黔教合KY字［2020］097);贵州省科技厅学术新苗培养及创新探索项目(黔科合平台人［2017］5727-06号);贵州省普通高等学校电子制造产学研基地(［2014］230);贵州省大学生创新创业训练计划项目(202110664026)

Unlabeled sequential three-way decisions model based on TOPSIS

YANG Jie1,2*, LUO Tian1, LI Yang-jun1   

  1. 1. School of Physics and Electronic Science, Zunyi Normal University, Zunyi 563002, Guizhou, China;
    2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Post and Telecommunications, Chongqing 400065, China
  • Published:2022-03-15

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摘要： 为解决无标签信息系统中已知接受域对象数的决策问题,提出了无标签信息系统的序贯三支决策模型。首先采用TOPSIS算法进行数据预处理;其次根据数据集特征提出期望密度差的定义,并提出了无标签三支决策的构建算法,给出数据驱动下的决策阈值选择方法;同时在此基础上进一步建立了无标签序贯三支决策模型。案例分析显示,在属性值动态更新的情况下,序贯三支决策阈值具有自适应性。最后,相关实验验证了该模型的有效性。

关键词: 序贯三支决策, 无标签信息系统, 数据驱动, 期望密度差

Abstract: To solve the issue of given accepted number for UIS, a unlabeled S3WD model is established. First, using TOPSIS algorithm for data pre-processing. Then, according to the data distribution, the concept of expectation density difference is defined to propose an algorithm of the unlabeled 3WD, and a selection method of the decision thresholds under data-driven is proposed. Meanwhile, the unlabeled S3WD model is further established. Next, the case analysis shows that the S3WD thresholds are adaptive under dynamic updating attributes. Finally, related experiments verify the validity of the model.

Key words: sequential three-way decision, unlabeled information system, data-driven, expectation density difference

中图分类号: 

  • TP311
[1] HWANG C L, YOON K. Methods for multiple attribute decision making[M]. Berlin: Springer, 1981:58-191.
[2] BORAN F E, GENC S, KURT M, et al. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method[J]. Expert Systems with Applications, 2009, 36(8):11363-11368.
[3] BUYUKOZKAN G, GULERYUZ S. An application of intuitionistic fuzzy TOPSIS on mobile phone selection[C] //2015 IEEE International Conference on Fuzzy Systems(FUZZ-IEEE). New York: IEEE, 2015: 1-8.
[4] SOLANKI R, GULATI G, TIWARI A, et al. A correlation based intuitionistic fuzzy TOPSIS method on supplier selection problem[C] //2016 IEEE International Conference on Fuzzy Systems(FUZZ-IEEE). New York: IEEE, 2016: 2106-2112.
[5] 夏勇其,吴祈宗. 一种混合型多属性决策问题的TOPSIS方法[J]. 系统工程学报, 2004, 19(6):630-634. XIA Yongqi, WU Qizong. A technique of order preference by similarity to ideal solution for hybrid multiple attribute decision making problems[J]. Journal of Systems Engineering, 2004, 19(6):630-634.
[6] 孙作雨,于洪,王国胤. 基于TOPSIS的三支决策评价模型[J]. 数据采集与处理, 2018, 33(5):891-899. SUN Zuoyu, YU Hong, WANG Guoyin. Three-way decision evaluation system based on TOPSIS[J]. Journal of Data Acquisition and Processing, 2018, 33(5):891-899.
[7] 孙作雨. 面向分层组织的多粒度三支决策评价问题研究[D]. 重庆: 重庆邮电大学, 2018. SUN Zuoyu. Study on multi-granularity three-way decision evaluation method for hierarchical organization[D]. Chongqing: Chongqing University of Post and Telecommunications, 2018.
[8] YAO Yiyu. Probabilistic approaches to rough sets[J]. Expert Systems, 2010, 20(5):287-297.
[9] YAO Yiyu. Three-way decisions with probabilistic rough sets[J]. Information Sciences, 2010, 180(3):341-353.
[10] YAO Yiyu. Set-theoretic models of three-way decision[J]. Granular Computing, 2021, 6(1):133-148.
[11] YANG Bo, LI Jinhai. Complex network analysis of three-way decision researches[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(5):973-987.
[12] LI Huaxiong, ZHANG Libo, ZHOU Xianzhong, et al. Cost-sensitive sequential three-way decision modeling using a deep neural network[J]. International Journal of Approximate Reasoning, 2017, 85:68-78.
[13] HAO Chen, LI Jinhai, FAN Min, et al. Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions[J]. Information Sciences, 2017, 415:213-232.
[14] ZHANG Qinghua, XIA Deyou, WANG Guoyin. Three-way decision model with two types of classification errors[J]. Information Sciences, 2017, 420:431-453.
[15] CHEN Jie, XU Yang, ZHAO Shu, et al. AH3: an adaptive hierarchical feature representation model for three-way decision boundary processing[J]. International Journal of Approximate Reasoning, 2021, 130:259-272.
[16] 张刚强,刘群,纪良浩. 基于序贯三支决策的多粒度情感分类方法[J]. 计算机科学, 2018, 45(12):153-159. ZHANG Gangqiang, LIU Qun, JI Lianghao. Multi-granularity sentiment classification method based on sequential three-way decisions[J]. Computer Science, 2018, 45(12):153-159.
[17] WANG Pingxin, LIU Qiang, WANG Kangkang. A three-way clustering method based on ensemble strategy and three-way decision[J]. Information Science, 2019, 10(2):59.
[18] 薛占熬, 韩丹杰, 吕敏杰, 等. 一种新的基于粒度重要度的三支决策模型[J]. 计算机科学, 2019, 46(2):245-250. XUE Zhanao, HAN Danjie, LV Minjie, et al. New three-way decisions model based on granularity importance degree[J]. Computer Science, 2019, 46(2):245-250.
[19] LI Huaxiong, ZHANG Libo, HUANG Bing, et al. Sequential three-way decision and granulation for cost-sensitive face recognition[J]. Knowledge-Based Systems, 2016, 91:241-251.
[20] YAO Yiyu, DENG Xiaofei. Sequential three-way decisions with probabilistic rough sets[C] //Proceedings of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing. New York: IEEE, 2011: 120-125.
[21] YANG Xin, LI Tianrui, FUJITA H, et al. A unified model of sequential three-way decisions and multilevel incremental processing[J]. Knowledge-Based Systems, 2017, 134:172-188.
[22] ZHANG Qinghua, YANG Chenchen, WANG Guoyin. A sequential three-way decision model with intuitionistic fuzzy numbers[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(5):2640-2652.
[23] JU Hengrong, PEDRYCZ W, LI Huaxiong, et al. Sequential three-way classifier with justifiable granularity[J]. Knowledge-Based Systems, 2019, 163:103-119.
[24] PAWLAK Z. Rough classification[J]. International Journal of Man Machine Studies, 1984, 20(5):469-483.
[1] 孙忠贵1,陈杰2. 数据驱动核的非局部均值滤波器改进[J]. 山东大学学报（理学版）, 2014, 49(05): 24-27.
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