南京大学学报(自然科学版) ›› 2021, Vol. 57 ›› Issue (1): 130–140.doi: 10.13232/j.cnki.jnju.2021.01.014

• • 上一篇    

基于熵的多尺度决策系统的最优尺度选择

郑嘉文1, 吴伟志1,2(), 包菡1, 谭安辉1,2   

  1. 1.浙江海洋大学数理与信息学院,舟山,316022
    2.浙江省海洋大数据挖掘与应用重点实验室,浙江海洋大学,舟山,316022
  • 收稿日期:2020-08-28 出版日期:2021-01-21 发布日期:2021-01-21
  • 通讯作者: 吴伟志 E-mail:wuwz@zjou.edu.cn
  • 作者简介:E⁃mail:wuwz@zjou.edu.cn
  • 基金资助:
    国家自然科学基金(61976194);浙江省自然科学基金(LY18F030017)

Entropy based optimal scale selection for multi⁃scale decision systems

Jiawen Zheng1, Weizhi Wu1,2(), Han Bao1, Anhui Tan1,2   

  1. 1.School of Mathematics,Physics and Information Science,Zhejiang Ocean University,Zhoushan,316022,China
    2.Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province,Zhejiang Ocean University,Zhoushan,316022,China
  • Received:2020-08-28 Online:2021-01-21 Published:2021-01-21
  • Contact: Weizhi Wu E-mail:wuwz@zjou.edu.cn

摘要:

粒计算模拟人类思考问题的自然模式是当今人工智能领域非常活跃的研究方向,在大数据挖掘和知识发现方面有独特的优势.针对多尺度决策系统的知识表示与知识获取问题,提出用信息熵角度研究多尺度信息系统的最优尺度选择问题.首先,定义多尺度信息系统的熵最优尺度与多尺度决策系统的广义决策熵最优尺度的概念;其次,讨论新提出的最优尺度概念与传统最优尺度概念之间的关系,证明在多尺度信息系统中传统的最优尺度与熵最优尺度是等价的;在协调多尺度决策系统中,证明传统的最优尺度与熵最优尺度也是等价的.而在不协调多尺度决策系统中,传统的最优尺度与熵最优尺度不等价,进而引入广义决策熵最优尺度,并证明广义决策最优尺度与广义决策熵最优尺度是等价的.

关键词: 粒计算, 信息熵, 多尺度信息系统, 粗糙集

Abstract:

Granular Computing (GrC),which imitates human being's thinking,is currently a vivid direction in the research fields of artificial intelligence. It shows great promise as a new way for data mining and knowledge discovery in the context of big data. To solve the problem of knowledge representation and knowledge acquisition in multi?scale decision systems,optimal scale selection in multi?scale information systems from the perspective of information entropy is proposed. The concepts of entropy optimal scale in multi?scale information systems and generalized decision entropy optimal scale in multi?scale decision systems are first defined. Relationships between new notions and classical ones are then examined. It is shown that in multi?scale information systems a scale is optimal if and only if it is entropy optimal. It is proved in consistent multi?scale decision systems a scale is optimal if and only if it is entropy optimal and while in inconsistent multi?scale decision systems the concept of the traditional optimal scale is not equivalent to that of the entropy optimal scale. By introducing the notion of generalized decision entropy optimal scale in inconsistent multi?scale decision systems,it is examined that the notion of generalized decision optimal scale is equivalent to that of the generalized decision entropy optimal scale.

Key words: granular computing, information entropy, multi?scale information systems, rough sets

中图分类号: 

  • TP18

表1

一个多尺度信息系统"

Ua11a12a13a21a22a23
x11BS1BS
x22GS1BS
x32GS1BS
x43FM2FS
x53FM2FS
x64EL3EL

表2

一个多尺度决策系统"

Ua11a12a13a21a22a23d
x11BS4BL1
x22BS3FS1
x33FS2FS2
x44GL1GS1
x51BS4BL1
x62BS3FS1
x73FS2FS2
x84GL1GS1

表3

一个多尺度决策系统"

Ua11a12a21a22d?C1?C2
x11B1Y11,21,2
x21B1Y21,21,2
x32B2Y21,21,2
x42B2Y21,21,2
x52B2Y11,21,2
x63F2Y222
x73F2Y222
x84G3N222
1 梁吉业,钱宇华,李德玉等. 大数据挖掘的粒计算理论与方法. 中国科学:信息科学,2015,45(11):1355-1369.
Liang J Y,Qian Y H,Li D Y,et al. (Theory and method of granular computing for big data mining. Scientia Sinica (Informationis),2015,45(11):1355-1369.
2 Chen C L P,Zhang C Y. Data intensive applications,
challenges,techniques and technologies:a survey on big data. Information Sciences,2014,275:314-347.
3 苗夺谦,张清华,钱宇华等. 从人类智能到机器实现模型:粒计算理论与方法. 智能系统学报,2016,11(6):743-757.
Miao D Q,Zhang Q H,Qian Y H,et al. From human intelligence to machine implementation model:theories and applications based on granular computing. CAAI Transactions on Intelligent Systems,2016,11(6):743-757.
4 徐计,王国胤,于洪. 基于粒计算的大数据处理. 计算机学报,2015,38(8):1497-1517.
Xu J,Wang G Y,Yu H. Review of big data processing based on granular computing. Chinese Journal of Computers,2015,38(8):1497-1517.
5 陈德刚,徐伟华,李金海等. 粒计算基础教程. 北京:科学出版社,2020,126.
6 Pawlak Z. Rough sets:theoretical aspects of reasoning about data. Boston:Kluwer Academic Publishers,1991,229.
7 Wu W Z,Leung Y. Theory and applications of granular labelled partitions in multi?scale decision tables. Information Sciences,2011,181(18):3878-3897.
8 Li F,Hu B Q. A new approach of optimal scale selection to multi?scale decision tables. Information Sciences,2017,381:193-208.
9 Li F,Hu B Q,Wang J. Stepwise optimal scale selection for multi?scale decision tables via attribute significance. Knowledge?Based Systems,2017,129:4-16.
10 Wu W Z,Leung Y. Optimal scale selection for multi?scale decision tables. International Journal of Approximate Reasoning,2013,54(8):1107-1129.
11 Wu W Z,Qian Y H,Li T J,et al. On rule acquisition in incomplete multi?scale decision tables. Information Sciences,2017,378:282-302.
12 Huang B,Li H X,Feng G F,et al. Dominance?based rough sets in multi?scale intuitionistic fuzzy decision tables. Applied Mathematics and Computation,2019,348:487-512.
13 Huang B,Wu W Z,Yan J J,et al. Inclusion measure?based multi?granulation decision?theoretic rough sets in multi?scale intuitionistic fuzzy information tables. Information Sciences,2020,507:421-448.
14 She Y H,Li J H,Yang H L. A local approach to rule induction in multi?scale decision tables. Knowledge?Based Systems,2015,89:398-410.
15 Hao C,Li J H,Fan M,et al. Optimal scale selection in dynamic multi?scale decision tables based on sequential three?way decisions. Information Sciences,2017,415-416:213-232.
16 Luo C,Li T R,Huang Y Y,et al. Updating three?way decisions in incomplete multi?scale information systems. Information Sciences,2019,476:274-289.
17 Chen Y S,Li J J,Huang J X. Matrix method for the optimal scale selection of multi?scale information decision systems. Mathematics,2019,7(3):290.
18 Zhang X Q,Zhang Q H,Cheng Y L,et al. Optimal scale selection by integrating uncertainty and cost?sensitive learning in multi?scale decision tables. International Journal of Machine Learning and Cybernetics,2020,11(5):1095-1114.
19 Xie J P,Yang M H,Li J H,et al. Rule acquisition and optimal scale selection in multi?scale formal decision contexts and their applications to smart city. Future Generation Computer Systems,2018,83:564-581.
20 顾沈明,顾金燕,吴伟志等. 不完备多粒度决策系统的局部最优粒度选择. 计算机研究与发展,2017,54(7):1500-1509.
Gu S M,Gu J Y,Wu W Z,et al. Local optimal granualarity selections in incomplete multi?granular decision system. Journal of Computer Research and Development,2017,54(7):1500-1509.
21 吴伟志,庄宇斌,谭安辉等. 不协调广义多尺度决策系统的尺度组合. 模式识别与人工智能,2018,31(6):485-494.
Wu W Z,Zhuang Y B,Tan A H,et al. Scale combinations in inconsistent generalized multi?scale decision systems. Pattern Recognition and Artificial Intelligence,2018,31(6):485-494.
22 吴伟志,杨丽,谭安辉等. 广义不完备多粒度标记决策系统的粒度选择. 计算机研究与发展,2018,55(6):1263-1272.
Wu W Z,Yang L,Tan A H,et al. Granularity selections in generalized incomplete multi?granular labeled decision systems. Journal of Computer Research and Development,2018,55(6):1263-1272.
23 吴伟志,陈超君,李同军等. 不协调多粒度标记决策系统最优粒度的对比. 模式识别与人工智能,2016,29(12):1095-1103.
Wu W Z,Chen C J,Li T J,et al. Comparative study on optimal granularities in inconsistent multi?granular labeled decision systems. Pattern Recognition and Artificial Intelligence,2016,29(12):1095-1103.
24 吴伟志,陈颖,徐优红等. 协调的不完备多粒度标记决策系统的最优粒度选择. 模式识别与人工智能,2016,29(2):108-115.
Wu W Z,Chen Y,Xu Y H,et al. Optimal granularity selections in consistent incomplete multi?granular labeled decision systems. Pattern Recognition and Artificial Intelligence,2016,29(2):108-115.
25 Shannon C E. A mathematical theory of communication. Bell System Technical Journal,1948,27(3-4):373-423.
26 苗夺谦,李德毅,姚一豫等. 不确定性与粒计算. 北京:科学出版社,2011,171.
27 苗夺谦,王珏. 粗糙集理论中概念与运算的信息表示. 软件学报,1999,10(2):113-116.
Miao D Q,Wang J. An information representation of the concepts and operations in rough set theory. Journal of Software,1999,10(2):113-116.
28 苗夺谦,王珏. 粗糙集理论中知识粗糙性与信息熵关系的讨论. 模式识别与人工智能,1998,11(1):34-40.
Miao D Q,Wang J. On the relationships between information entropy and roughness of know?ledge in rough set theory. Pattern Recognition and Artificial Intelligence,1998,11(1):34-40.
29 梁吉业,李德玉. 信息系统中的不确定性与知识获取. 北京:科学出版社,2005,118.
30 王国胤,于洪,杨大春. 基于条件信息熵的决策表约简. 计算机学报,2002,25(7):759-766.
Wang G Y,Yu H,Yang D C. Decision table reduction based on conditional information entropy. Chinese Journal of Computers,2002,25(7):759-766.
31 张清华,王国胤,胡军. 多粒度知识获取与不确定性度量. 北京:科学出版社,2013,244.
[1] 刘琼, 代建华, 陈姣龙. 区间值数据的代价敏感特征选择[J]. 南京大学学报(自然科学版), 2021, 57(1): 121-129.
[2] 李同军,于洋,吴伟志,顾沈明. 经典粗糙近似的一个公理化刻画[J]. 南京大学学报(自然科学版), 2020, 56(4): 445-451.
[3] 任睿,张超,庞继芳. 有限理性下多粒度q⁃RO模糊粗糙集的最优粒度选择及其在并购对象选择中的应用[J]. 南京大学学报(自然科学版), 2020, 56(4): 452-460.
[4] 王宝丽,姚一豫. 信息表中约简补集对及其一般定义[J]. 南京大学学报(自然科学版), 2020, 56(4): 461-468.
[5] 王丽娟,丁世飞,丁玲. 基于迁移学习的软子空间聚类算法[J]. 南京大学学报(自然科学版), 2020, 56(4): 515-523.
[6] 姚宁, 苗夺谦, 张远健, 康向平. 属性的变化对于流图的影响[J]. 南京大学学报(自然科学版), 2019, 55(4): 519-528.
[7] 程永林, 李德玉, 王素格. 基于极大相容块的邻域粗糙集模型[J]. 南京大学学报(自然科学版), 2019, 55(4): 529-536.
[8] 张龙波, 李智远, 杨习贝, 王怡博. 决策代价约简求解中的交叉验证策略[J]. 南京大学学报(自然科学版), 2019, 55(4): 601-608.
[9] 郭英杰, 胡峰, 于洪, 张红亮. 基于时间粒的铝电解过热度预测模型[J]. 南京大学学报(自然科学版), 2019, 55(4): 624-632.
[10] 李藤, 杨田, 代建华, 陈鸰. 基于模糊区分矩阵的结直肠癌基因选择[J]. 南京大学学报(自然科学版), 2019, 55(4): 633-643.
[11] 张 婷1,2,张红云1,2*,王 真3. 基于三支决策粗糙集的迭代量化的图像检索算法[J]. 南京大学学报(自然科学版), 2018, 54(4): 714-.
[12] 敬思惠,秦克云*. 决策系统基于特定决策类的上近似约简[J]. 南京大学学报(自然科学版), 2018, 54(4): 804-.
[13] 胡玉文1,2,3*,徐久成1,2,张倩倩1,2. 决策演化集的膜结构抑制剂[J]. 南京大学学报(自然科学版), 2018, 54(4): 810-.
[14] 陶玉枝1,2,赵仕梅1,2,谭安辉1,2*. 一种基于决策表约简的集覆盖问题的近似解法[J]. 南京大学学报(自然科学版), 2018, 54(4): 821-.
[15]  方 宇1,闵 帆1*,刘忠慧1,杨 新2.  序贯三支决策的代价敏感分类方法[J]. 南京大学学报(自然科学版), 2018, 54(1): 148-.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!