您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（理学版）》

《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (9): 33-45.doi: 10.6040/j.issn.1671-9352.0.2021.457

• • 上一篇    下一篇

多粒度层次序贯三支决策模型研究

钱进1,2,汤大伟1*,洪承鑫2   

  1. 1.江苏科技大学计算机学院, 江苏 镇江 212100;2.华东交通大学软件学院, 江西 南昌 330013
  • 发布日期:2022-09-15
  • 作者简介:钱进(1975— ),男,教授,博士,研究方向为三支决策、粗糙集、云计算、大数据挖掘等. E-mail:qjqjlqyf@163.com*通信作者简介:汤大伟(1996— ),男,硕士研究生,研究方向为粒计算与三支决策. E-mail:davydavy2021@163.com
  • 基金资助:
    国家自然科学基金资助项目(62066014);江西省自然科学基金资助项目(20202BABL202018);江苏省“青蓝工程”中青年学术带头人培养项目;江西省“双千计划”项目

Research on multi-granularity hierarchical sequential three-way decision model

QIAN Jin1,2, TANG Da-wei1*, HONG Cheng-xin2   

  1. 1.School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China;
    2. School of Software, East China Jiaotong University, Nanchang 330013, Jiangxi, China
  • Published:2022-09-15

PDF (PC)

247

摘要： 现有的知识获取算法所挖掘出的规则太多,不易理解;规则描述太过具体,容易造成过拟合。为此,本文提出了多粒度层次序贯三支决策模型。首先引入概念层次树将目标概念泛化,构建多层次决策表,并设计了多粒度层次序贯三支决策模型,从多视角、多层次计算3个概率区域并获取相应的泛化层次决策规则。最后,通过实验证明了模型的有效性。本模型为知识获取提供了新的视角并丰富了多粒度三支决策的研究。

关键词: 知识获取, 三支决策, 多粒度粗糙集, 多层次决策表, 概念层次树

Abstract: The existing algorithms generate a large number of the decision rules, which is not easy to be understood. Moreover, the descriptions of the rules are too specific, which easily lead to overfitting. To this end, a multi-granularity hierarchical sequential three-way decision model is proposed. Firstly, we generalize the target concept through the concept hierarchy tree and construct the multi-hierarchical decision table. Then, we construct the multi-granularity hierarchical sequential three-way decision model, calculate the three probabilistic regions, and acquire the corresponding the generalized rules from the multi-view and multi-level. Finally, the effectiveness of our model is verified by experiments. This model provides a new perspective for knowledge acquisition and enriches the research of multi-granularity three-way decisions.

Key words: knowledge acquisition, three-way decision, multi-granularity rough set, multi-hierarchical decision table, concept hierarchy tree

中图分类号: 

  • TP18
[1] ZADEH L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and Systems, 1997, 90(2):111-127.
[2] 李金海,王飞,吴伟志,等. 基于粒计算的多粒度数据分析方法综述[J]. 数据采集与处理, 2021, 36(3):418-435. LI Jinhai, WANG Fei, WU Weizhi, et al. Review of multi-granularity data analysis methods based on granular computing[J]. Journal of Data Acquisition and Processing, 2021, 36(3):418-435.
[3] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information Sciences, 2009, 180(6):949-970.
[4] QIAN Yuhua, ZHANG Hu, SANG Yanli, et al. Multigranulation decision-theoretic rough sets[J]. International Journal Approximate Reasoning, 2014, 55(1):225-237.
[5] 钱进. 多粒度决策粗糙集模型研究[J]. 郑州大学学报(理学版), 2018, 50(1):33-38. QIAN Jin. Research on multigranulation decision-theoretic rough set models[J]. Journal of Zhengzhou University(Natural Science Edition), 2018, 50(1):33-38.
[6] 万青,马盈仓,魏玲. 基于多粒度的多源数据知识获取[J]. 山东大学学报(理学版), 2020, 55(1):41-50. WAN Qing, MA Yingcang, WEI Ling. Knowledge acquisition of multi-source data based on multigranularity[J]. Journal of Shangdong University(Natural Science), 2020, 55(1):41-50.
[7] 张文娟,李进金,林艺东. 基于图的悲观多粒度粗糙集粒度约简[J]. 山东大学学报(理学版), 2021, 56(1):60-67. ZHANG Wenjuan, LI Jinjin, LIN Yidong. Graph-based granularity reduction in pessimistic multi-granulation rough set[J]. Journal of Shangdong University(Natural Science), 2021, 56(1):60-67.
[8] FENG Qinrong, MIAO Duoqian, CHENG Yi. Hierarchical decision rules mining[J]. Expert Systems with Applications, 2010, 37(4):2081-2091.
[9] WU Weizhi, LEUNG Yee. Theory and applications of granular labeled partitions in multi-scale decision tables[J]. Information Science, 2011, 181(18):3878-3897.
[10] YAO Yiyu. Three-way decisions with probabilistic rough sets[J]. Information Sciences, 2010, 180(3):341-353.
[11] 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. Piscataway: IEEE, 2011: 120-125.
[12] YAO Yiyu. Tri-level thinking: models of three-way decision[J]. International Journal of Machine Learning and Cybernetics, 2020, 11:947-959.
[13] 范琴,刘盾,叶晓庆. 基于序贯三支决策的代价敏感文本情感分析方法[J]. 模式识别与人工智能, 2020, 33(8):732-742. FAN Qin, LIU Dun, YE Xiaoqing. Cost-sensitive text sentiment analysis based on sequential three-way decision[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(8):732-742.
[14] 刘琳,魏玲,钱婷. 决策形式背景中具有置信度的三支规则提取[J]. 山东大学学报(理学版), 2017, 52(2):101-110. LIU Lin, WEI Ling, QIAN Ting. Three-way rules extraction in formal decision contexts with confidence[J]. Journal of Shangdong University(Natural Science), 2017, 52(2):101-110.
[15] LUO Junfang, HU Mengjun, QIN Keyun. Three-way decision with incomplete information based on similarity and satisfiability[J]. International Journal Approximate Reasoning, 2020, 120:151-183.
[16] 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.
[17] SAVCHENKO A V. Sequential three-way decisions in multi-category image recognition with deep features based on distance factor[J]. Information Sciences, 2019, 489:18-36.
[18] LI Jinhai, HUANG Chenchen, QI Jianjun, et al. Three-way cognitive concept learning via multi-granularity[J]. Information Sciences, 2017, 378(1):244-263.
[19] YANG Xin, LI Tianrui, FUJITA Hamido, et al. A unified model of sequential three-way decisions and multilevel incremental processing[J]. Knowledge-Based Systems, 2017, 134(15):172-188.
[20] XU Yi, TANG Jingxin, WANG Xusheng. Three sequential multi-class three-way decision models[J]. Information Sciences, 2020, 537:62-90.
[21] ZHANG Qinghua, PANG Guohong, WANG Guoyin. A novel sequential three-way decisions model based on penalty function[J]. Knowledge-Based Systems, 2020, 192(15):105350.
[22] QIAN Jin, LIU Caihui, MIAO Duoqian, et al. Sequential three-way decisions via multi-granularity[J]. Information Sciences, 2020, 507:606-629.
[23] 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, 415/416:213-232.
[24] CHENG Yunlong, ZHANG Qinghua, WANG Guoyin, et al. Optimal scale selection and attribute reduction in multi-scale decision tables based on threeway decision[J]. Information Sciences, 2020, 541:36-59.
[1] 巩增泰,他广朋. 直觉模糊集所诱导的软集语义及其三支决策[J]. 《山东大学学报(理学版)》, 2022, 57(8): 68-76.
[2] 施极,索中英. 基于区间数层次分析法的损失函数确定方法[J]. 《山东大学学报(理学版)》, 2022, 57(5): 28-37.
[3] 杨洁,罗天,李阳军. 基于TOPSIS的无标签序贯三支决策模型[J]. 《山东大学学报(理学版)》, 2022, 57(3): 41-48.
[4] 李敏,杨亚锋,雷宇,李丽红. 基于可拓域变化代价最小的最优粒度选择[J]. 《山东大学学报(理学版)》, 2021, 56(2): 17-27.
[5] 张文娟,李进金,林艺东. 基于图的悲观多粒度粗糙集粒度约简[J]. 《山东大学学报(理学版)》, 2021, 56(1): 60-67.
[6] 余鹰,吴新念,王乐为,张应龙. 基于标记相关性的多标记三支分类算法[J]. 《山东大学学报(理学版)》, 2020, 55(3): 81-88.
[7] 姬儒雅,魏玲,任睿思,赵思雨. 毕达哥拉斯模糊三支概念格[J]. 《山东大学学报(理学版)》, 2020, 55(11): 58-65.
[8] 张海洋,马周明,于佩秋,林梦雷,李进金. 多粒度粗糙集近似集的增量方法[J]. 《山东大学学报(理学版)》, 2020, 55(1): 51-61.
[9] 刘国涛,张燕平,徐晨初. 一种优化覆盖中心的三支决策模型[J]. 山东大学学报（理学版）, 2017, 52(3): 105-110.
[10] 汪小燕,沈家兰,申元霞. 基于加权粒度和优势关系的程度多粒度粗糙集[J]. 山东大学学报（理学版）, 2017, 52(3): 97-104.
[11] 张聪, 于洪. 一种三支决策软增量聚类算法[J]. 山东大学学报（理学版）, 2014, 49(08): 40-47.
[12] 田海龙, 朱艳辉, 梁韬, 马进, 刘璟. 基于三支决策的中文微博观点句识别研究[J]. 山东大学学报（理学版）, 2014, 49(08): 58-65.
[13] 杜丽娜, 徐久成, 刘洋洋, 孙林. 基于三支决策风险最小化的风险投资评估应用研究[J]. 山东大学学报（理学版）, 2014, 49(08): 66-72.
[14] 张里博, 李华雄, 周献中, 黄兵. 人脸识别中的多粒度代价敏感三支决策[J]. 山东大学学报（理学版）, 2014, 49(08): 48-57.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 杨军. 金属基纳米材料表征和纳米结构调控[J]. 山东大学学报（理学版）, 2013, 48(1): 1 -22 .
[2] 何海伦， 陈秀兰*. 变性剂和缓冲系统对适冷蛋白酶MCP-01和中温蛋白酶BP-01构象影响的圆二色光谱分析何海伦， 陈秀兰*[J]. 山东大学学报（理学版）, 2013, 48(1): 23 -29 .
[3] 赵君1,赵晶2,樊廷俊1*,袁文鹏1,3,张铮1,丛日山1. 水溶性海星皂苷的分离纯化及其抗肿瘤活性研究[J]. J4, 2013, 48(1): 30 -35 .
[4] 孙小婷1,靳岚2*. DOSY在寡糖混合物分析中的应用[J]. J4, 2013, 48(1): 43 -45 .
[5] 罗斯特,卢丽倩,崔若飞,周伟伟,李增勇*. Monte-Carlo仿真酒精特征波长光子在皮肤中的传输规律及光纤探头设计[J]. J4, 2013, 48(1): 46 -50 .
[6] 杨伦，徐正刚，王慧*，陈其美，陈伟，胡艳霞，石元，祝洪磊，曾勇庆*. RNA干扰沉默PID1基因在C2C12细胞中表达的研究[J]. J4, 2013, 48(1): 36 -42 .
[7] 冒爱琴1, 2, 杨明君2, 3, 俞海云2, 张品1, 潘仁明1*. 五氟乙烷灭火剂高温热解机理研究[J]. J4, 2013, 48(1): 51 -55 .
[8] 杨莹,江龙*,索新丽. 容度空间上保费泛函的Choquet积分表示及相关性质[J]. J4, 2013, 48(1): 78 -82 .
[9] 李永明1, 丁立旺2. PA误差下半参数回归模型估计的r-阶矩相合[J]. J4, 2013, 48(1): 83 -88 .
[10] 董伟伟. 一种具有独立子系统的决策单元DEA排序新方法[J]. J4, 2013, 48(1): 89 -92 .
版权所有 © 2018 《山东大学学报(理学版)》编辑部　
地址: 山东省济南市山大南路27号山东大学中心校区（250100）　电话: +86-0531-88366917　E-mail: xblxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发