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《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (7): 1-13.doi: 10.6040/j.issn.1671-9352.0.2022.132

• •    

分离模糊集合(A(-overF),AF)与模糊信息智能融合

史开泉1*,李守伟2   

  1. 1.山东大学数学学院, 山东 济南 250100;2.山东师范大学商学院, 山东 济南 250014
  • 发布日期:2022-06-29
  • 作者简介:史开泉(1945— ), 男, 教授, 博士生导师, 研究方向为模糊集合理论与应用、信息融合理论与数学交叉研究. E-mail:shikq@sdu.edu.cn*通信作者
  • 基金资助:
    国家自然科学基金资助项目(71663010);山东省自然科学基金资助项目(ZR2019MG015)

Separated fuzzy set (A(-overF),AF) and the intelligent fusion of fuzzy information

SHI Kai-quan1*, LI Shou-wei2   

  1. 1. School of Mathematics, Shandong University, Jinan 250100, Shandong, China;
    2. School of Business, Shandong Normal University, Jinan 250014, Shandong, China
  • Published:2022-06-29

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摘要： 利用内-分离论域、外-分离论域与分离论域,改进了L.A.Zadeh模糊集合,提出了由内-分离模糊集合与外-分离模糊集合共同组成的分离模糊集合。利用分离模糊集合,给出了模糊信息融合的生成及其属性关系;给出了模糊信息融合中的信息筛以及信息筛分准则,进而提出了模糊信息外-融合-智能检索算法;利用这些理论结果,给出了模糊信息外-融合在疾病分类智能检索中的应用。

关键词: 分离论域, 属性合取, 分离模糊集合, 模糊信息融合, 智能检索, 应用

Abstract: By using internal-separated universe, outer-separated universe and separated universe, L.A.Zadeh fuzzy set is improved, and a separated fuzzy set composed of internal-separated fuzzy set and outer-separated fuzzy set is proposed. Based on the separated fuzzy set, the generation of fuzzy information fusion and its attribute relationship are given. The information screening and its criteria in fuzzy information fusion are given, and then the outer-fusion intelligent retrieval algorithm of fuzzy information is proposed. By using these theoretical results, the application of fuzzy information outer-fusion in intelligent retrieval of disease classification is given.

Key words: separated universe, attribute conjunction, separated fuzzy set, fuzzy information fusion, intelligent retrieval, application

中图分类号: 

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[1] ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3):338-353.
[2] ZADEH L A. Probability measures of fuzzy events[J]. Journal of Mathematical Analysis and Applications, 1968, 23(2):421-427.
[3] ZADEH L A. Similarity relations and fuzzy orderings[J]. Information Sciences, 1971, 3(2):177-200.
[4] 史开泉,崔玉泉. 双枝模糊决策与决策加密-认证[J]. 中国科学E, 2003, 33(2):154-163. SHI Kaiquan, CUI Yuquan. Both-branch fuzzy secision and decision encryption-authentication[J]. Science in China Series E, 2003, 33(2):154-163.
[5] SHI K Q, CUI Y Q. Both-branch fuzzy decision and decision encryption-authentication[J]. Science in China: F, 2003, 46(2):90-103.
[6] SHI K Q, DAVID K W N G. Computation of fuzzy transitive clasure by S-K-Q-Δ method[J]. The Journal of Fuzzy Mathematics, 1993, 1(1):25-32.
[7] 史开泉,汪保明. Fuzzy传递闭包与Fuzzy分类矩阵的S-K-Q判定定理[J]. 模糊系统与数学, 1990, 4(2):92-97. SHI Kaiquan, WANG Baoming. S-K-Q critical theorems for fuzzy transitive closure R* and fuzzy classifying matrix R[J]. Fuzzy Systems and Mathematics, 1990, 4(2):92-97.
[8] MOLINA C, RUIZ M D, SERRANO J M. Representation by levels: an alternative to fuzzy sets for fuzzy data mining[J]. Fuzzy Sets and Systems, 2020, 401:113-132.
[9] HUITZIL I, BOBILLO F, GÓMEZ-ROMERO J, et al. Fudge: fuzzy ontology building with consensuated fuzzy datatypes[J]. Fuzzy Sets and Systems, 2020, 401:91-112.
[10] DALTERIO P, GARIBALDI J M, JOHN R I, et al. Constrained interval type-2 fuzzy sets[J]. IEEE Transactions on Fuzzy Systems, 2020, 29(5):1212-1225.
[11] DONG Q, GONG S, ZHU X. Imbalanced deep learning by minority class incremental rectification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(6):1367-1381.
[12] MATHEW J, PANG C K, LUO M, et al. Classification of imbalanced data by oversampling in kernel space of support vector machines[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9):4065-4076.
[13] GAO M, HONG X, HARRIS C. Construction of neurofuzzy models for imbalanced data classification[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(6):1472-1488.
[14] LIU S, ZHANG J, XIANG Y, et al. Fuzzy-based information decomposition for incomplete and imbalanced data learning[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(6):1476-1490.
[15] RAMENTOL E, VLUYMANS S, VERBIEST N, et al. IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(5):1622-1637.
[16] RI J H, LIU L, LIU Y, et al. Optimal weighted extreme learning machine for imbalanced learning with differential evolution[J]. IEEE Computational Intelligence Magazine, 2018, 13(3):32-47.
[17] MANGASARIAN O, MUSICANT D. Lagrangian support vector machines[J]. Journal of Machine Learning Research, 2001, 1(1):161-177.
[18] SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3):293-300.
[19] TSANG I, KWOK J, CHEUNG P M. Core vector machines: fast SVM training on very large data sets[J]. Journal of Machine Learning Research, 2005, 6(1):363-392.
[20] TSANG I W H, KWOK J T Y, ZURADA J M. Generalized core vector machines[J]. IEEE Transactions Neural Networks, 2006, 17(5):1126-1142.
[21] TSANG I W H, KOCSOR A, KWOK J T Y. Large-scale maximum margin discriminant analysis using core vector machines[J]. IEEE Transactions Neural Networks, 2008, 19(4):610-623.
[22] ANGELOV P P, ZHOU X. Evolving fuzzy-rule-based classifiers from data streams[J]. IEEE Transactions on Fuzzy Systems, 2008, 16(6):1462-1475.
[23] LESKI J. An ε-margin nonlinear classifier based on if-then rules[J]. IEEE Transactions on Systems, Man and Cybernetics: B, 2004, 34(1):68-76.
[24] LUGHOFER E, BUCHTALA O. Reliable all-pairs evolving fuzzy classifiers[J]. IEEE Transactions on Fuzzy Systems, 2013, 21(4):625-641.
[25] YANG X, ZHANG G, LU J, et al. A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(1):105-115.
[26] LESKI J. Fuzzy(c+p)-means clustering and its application to a fuzzy rule-based classifier: towards good generalization and good interpretability[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(4):802-812.
[27] HSU C W, LIN C J. A comparison of methods for multiclass support vector machines[J]. IEEE Transactions Neural Networks, 2002, 13(2):415-425.
[28] PEDRYCZ W. Fuzzy set technology in knowledge discovery[J]. Fuzzy Sets and Systems, 1998, 98(2):279-290.
[29] PEDRYCZ W. Cluster-centric fuzzy modeling[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(6):1585-1597.
[30] LESKI J. Fuzzy c-ordered-means clustering[J]. Fuzzy Sets and Systems, 2016, 286(1):114-133.
[31] LESKI J M, KOTAS M P. Linguistically defined clustering of data[J]. International Journal of Applied Mathematics and Computer Science, 2018, 28(3):545-557.
[32] 史开泉. P-集合[J]. 山东大学学报(理学版), 2008, 43(11):77-84. SHI Kaiquan. P-sets[J]. Journal of Shandong University(Natural Science), 2008, 43(11):77-84.
[33] SHI K Q. P-sets and its applications[J]. Advances in Systems Science and Applications, 2009, 9(2):209-219.
[34] 史开泉. P-集合, 逆P-集合与信息智能融合-过滤辨识[J]. 计算机科学, 2012, 39(4):1-13. SHI Kaiquan. P-sets, inverse P-sets and the intelligent fusion-filter identification of information[J]. Computer Science, 2012, 39(4):1-13.
[35] FAN C X, LIN H K. P-sets and the reasoning-identification of disaster information[J]. International Journal of Convergence Information Technology, 2012, 7(1):337-345.
[36] LIN H K, FAN C X. The dual form of P-reasoning and identification of unknown attribute[J]. International Journal of Digital Content Technology and its Applications, 2012, 6(1):121-131.
[37] 史开泉, 函数P-集合[J]. 山东大学学报(理学版), 2011, 46(2):62-69. SHI Kaiquan. Function P-sets[J]. Journal of Shandong University(Natural Science), 2011, 46(2):62-69.
[38] SHI K Q. Function P-sets[J]. International Journal of Machine Learning and Cybernetics, 2011, 2(4):281-288.
[39] TANG J H, ZHANG L, SHI K Q. Outer P-information law reasoning and its application in intelligent fusion and separating of information law[J]. Microsystem Technologies, 2018, 24(10):4389-4398.
[40] PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982, 11(5):341-356.
[41] 史开泉, 姚炳学. 函数S-粗集与规律辨识[J]. 中国科学F, 2008, 38(4):553-564. SHI Kaiquan, YAO Bingxue. Function S-rough sets and security-authentication of hiding law[J]. Science in China Series F: Information Sciences, 2008, 38(4):553-564.
[42] SHI K Q, YAO B X. Function S-rough sets and law identification[J]. Science in China Series F: Information Sciences, 2008, 51(5):499-510.
[43] 史开泉, 赵建立. 函数S-粗集与隐藏规律安全认证[J]. 中国科学E, 2008, 38(8):1234-1243. SHI Kaiquan, ZHAO Jianli. Function S-rough sets and security-authentication of hiding law[J]. Scinence in China E, 2008, 38(8):1234-1243.
[44] SHI K Q, ZHAO J L. Function S-rough sets and security-authentication of hiding law[J]. Scinence in China F, 2008, 51(7):924-935.
[45] WEI W, LIANG J Y. Information fusion in rough set theory: an overview[J]. Information Fusion, 2019, 48(8):107-118.
[46] WANG H J, ZHANG Q. Dynamic identification of coal-rock interface based on adaptive weight optimization and multi-sensor information fusion[J]. Information Fusion, 2019, 51(11):114-128.
[47] WANG H, XU Z S, ZENG X J. Hesitant fuzzy linguistic term sets for linguistic decision making: current developments, issues and challenges[J]. Information Fusion, 2018, 43(9):1-12.
[48] ZHANG S, XU Z S, HE Y. Operations and integrations of probabilistic hesitant fuzzy information in decision making[J]. Information Fusion, 2017, 38(11):1-11.
[49] WANG H, XU Z S. Admissible orders of typical hesitant fuzzy elements and their application in ordered information fusion in multi-criteria decision making[J]. Information Fusion, 2016, 29(5):98-104.
[50] 史开泉. 大数据结构-逻辑特征与大数据规律[J]. 山东大学学报(理学版), 2019, 54(2):1-29. SHI Kaiquan. Big data structure-logic characteristics and big data law[J]. Journal of Shandong University(Natural Science), 2019, 54(2):1-29.
[51] 刘纪芹, 史开泉. 大数据分解-融合及其智能获取[J]. 计算机科学, 2020, 47(6):66-73. LIU Jiqin, SHI Kaiquan. Big data decomposition-fusion and its intelligent acquisition[J]. Computer Science, 2020, 47(6):66-73.
[52] 郝秀梅, 史开泉. 大数据智能检索与大数据区块元智能分离[J]. 计算机科学, 2020, 47(11):113-121. HAO Xiumei, SHI Kaiquan. Big data intelligent retrieval and big data block element intelligence separation[J]. Computer Science, 2020, 47(11):113-121.
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