ISSN 0253-2778

CN 34-1054/N

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A rule activation method for extended belief rule base based on improved similarity measures

  • College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.01.003
  • Received Date: 18 May 2017
  • Rev Recd Date: 22 June 2017
  • Publish Date: 31 January 2018
    Abstract

  • Abstract

    When calculating negative individual matching degrees, there might appear negative values and all rules’ activation weights may be equal to zero. To address this problem, this paper introduces the Euclidean distance which is based on attribute weights and improves the traditional similarity computational formula. In addition, the traditional rule activation method activates all rules whose activation weights are greater than zero without considering inconsistency which exists in the activated rules, since the inconsistency of activated rules will weaken the reasoning performance of EBRB systems. Hence, considering the inconsistency existing in the activated rules, a new rule activation method of EBRB based on improved similarity measures is proposed. Compared with traditional rule activation method in the EBRB, the proposed approach activates rules by setting thresholds. And these activated rules are not only greater than zero but also have the smallest inconsistency. Finally, the pipeline leak detection problem and multiple public classification datasets have been employed to validate the efficiency of the new rule activation method. The experimental results show that the proposed method based on improved similarity measures can improve the reasoning accuracy of EBRB systems.

    Abstract

    When calculating negative individual matching degrees, there might appear negative values and all rules’ activation weights may be equal to zero. To address this problem, this paper introduces the Euclidean distance which is based on attribute weights and improves the traditional similarity computational formula. In addition, the traditional rule activation method activates all rules whose activation weights are greater than zero without considering inconsistency which exists in the activated rules, since the inconsistency of activated rules will weaken the reasoning performance of EBRB systems. Hence, considering the inconsistency existing in the activated rules, a new rule activation method of EBRB based on improved similarity measures is proposed. Compared with traditional rule activation method in the EBRB, the proposed approach activates rules by setting thresholds. And these activated rules are not only greater than zero but also have the smallest inconsistency. Finally, the pipeline leak detection problem and multiple public classification datasets have been employed to validate the efficiency of the new rule activation method. The experimental results show that the proposed method based on improved similarity measures can improve the reasoning accuracy of EBRB systems.
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    • References(21)
  • [1]
    周志杰, 杨剑波,胡昌华,等.置信规则库专家系统与复杂系统建模[M].北京: 科学出版社, 2011.
    [2]
    YANG J B, LIU J, WANG J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transactions on Systems, Man and Cybernetics Part A-Systems and Humans, 2006, 36(2): 266-285.
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    DEMPSTER A P. A generalization of Bayesian inference [J].Journal of the Royal Statistical Society B, 1968, 30(2): 205-247.
    [4]
    SHAFER G A. Mathematical Theory of Evidence [M]. Princeton: Princeton university press, 1979.
    [5]
    HWANG C L, YOON K S. Multiple attribute decision making: Methods and applications [J]. Wuropean Journal of Operational Research, 1981, 22(1): 22-34.
    [6]
    ZADEH L A. Fuzzy sets [J]. Information and control, 1965, 8(3): 338-353.
    [7]
    ZHOU Z J, HU C H, YANG J B, et al. Online updating belief rule based system for pipeline leak detection under expert intervention [J]. Expert Systems with Applications , 2008, 36(4): 7700-7709.
    [8]
    XU D L, LIU J, YANG J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection [J]. Expert Systems with Applications, 2007, 32(1): 103-113.
    [9]
    YANG J B, LIU J, XU D L, et al. Optimization models for training belief-rule-based systems [J]. IEEE Transactions on Systems, Man and Cybernetics, 2007, 37(4): 569-585.
    [10]
    YANG Y, FU C, CHEN Y W, et al. A belief rule based expert system for predicting consumer preference in new product development [J]. Knowledge Based Systems, 2016, 94(C): 338-353.
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    JIANG J, LI X, ZHOU Z J, et al. Weapon system capability assessment under uncertainty based on the evidential reasoning approach [J]. Expert Systems with Applications, 2011, 38(11): 13773-13784.
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    杨隆浩, 蔡芷铃, 黄志鑫,等. 出租车乘车概率预测的置信规则库推理方法[J]. 计算机科学与探索, 2015,9(8): 985-994.
    YANG L H, CAI Z L, HUANG Z X, et al. Belief rule-base inference methodology for predicting probability of taking taxi[J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(8): 985-994.
    [13]
    CHEN Y W, YANG J B, XU D L, et al. Inference analysis and adaptive training for belief rule based systems [J]. Expert Systems with Applications, 2011, 38(10): 12845-12860.
    [14]
    常瑞, 王红卫, 杨剑波. 基于梯度法与二分法的置信规则库参数训练方法 [J]. 系统工程, 2007: 287-291.
    CHANG R, WANG H W, YANG J B. An algorithm for training parameters in belief rule-bases based on the gradient and dichotomy methods [J]. Systems Engineering, 2007: 287-291.
    [15]
    苏群, 杨隆浩, 傅仰耿. 基于变速粒子群优化的置信规则库参数训练方法[J]. 计算机应用, 2014, 34(8): 2161-2165.
    SU Q, YANG L H, FU Y G. Parameter training approach based on variable particle swarm optimization for belief rule base[J]. Journal of Computer Application, 2014, 34(8): 2161-2165.
    [16]
    王韩杰, 杨隆浩, 傅仰耿, 等. 专家干预下置信规则库参数训练的差分进化算法[J]. 计算机科学, 2015, 42(5): 88-93.
    WANG H J, YANG L H ,FU Y G, et al. Differential evolutionary algorithm for parameter training of belief rule base under expert intervention[J]. Computer Science, 2015, 42(5):88-93.
    [17]
    LIU J, MARTINEZ L, CALZADA A, et al. A novel belief rule base representation, generation and its inference methodology [J]. Knowledge-Based Systems, 2013, 53: 129-141.
    [18]
    CALZADA A, LIU J, WANG H, et al. A new dynamic rule activation method for extended belief rule-based systems[J]. IEEE Transactions on Knowledge and data Engineering, 2015, 7(4): 880-888.
    [19]
    苏群, 杨隆浩, 傅仰耿, 等. 基于BK树的扩展置信规则库结构优化框架[J].计算机科学与探索, 2016, 10(2): 257-267.
    SU Q, YANG L H, FU Y G, et al. Structure optimization framework of extended belief rule base based on BK-tree[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(2): 257-267.
    [20]
    YANG L H, WANG Y M, SU Q, et al. Multi-attribute search framework for optimizing extended belief rule-based systems [J]. Information Science, 2016: 370-371.
    [21]
    FRANK A, ASUNCION A. UCI machine learning repository[EB/OL]. [2017-04-16]http://archive.ics.uci.edu/.
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    [1]
    周志杰, 杨剑波,胡昌华,等.置信规则库专家系统与复杂系统建模[M].北京: 科学出版社, 2011.
    [2]
    YANG J B, LIU J, WANG J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transactions on Systems, Man and Cybernetics Part A-Systems and Humans, 2006, 36(2): 266-285.
    [3]
    DEMPSTER A P. A generalization of Bayesian inference [J].Journal of the Royal Statistical Society B, 1968, 30(2): 205-247.
    [4]
    SHAFER G A. Mathematical Theory of Evidence [M]. Princeton: Princeton university press, 1979.
    [5]
    HWANG C L, YOON K S. Multiple attribute decision making: Methods and applications [J]. Wuropean Journal of Operational Research, 1981, 22(1): 22-34.
    [6]
    ZADEH L A. Fuzzy sets [J]. Information and control, 1965, 8(3): 338-353.
    [7]
    ZHOU Z J, HU C H, YANG J B, et al. Online updating belief rule based system for pipeline leak detection under expert intervention [J]. Expert Systems with Applications , 2008, 36(4): 7700-7709.
    [8]
    XU D L, LIU J, YANG J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection [J]. Expert Systems with Applications, 2007, 32(1): 103-113.
    [9]
    YANG J B, LIU J, XU D L, et al. Optimization models for training belief-rule-based systems [J]. IEEE Transactions on Systems, Man and Cybernetics, 2007, 37(4): 569-585.
    [10]
    YANG Y, FU C, CHEN Y W, et al. A belief rule based expert system for predicting consumer preference in new product development [J]. Knowledge Based Systems, 2016, 94(C): 338-353.
    [11]
    JIANG J, LI X, ZHOU Z J, et al. Weapon system capability assessment under uncertainty based on the evidential reasoning approach [J]. Expert Systems with Applications, 2011, 38(11): 13773-13784.
    [12]
    杨隆浩, 蔡芷铃, 黄志鑫,等. 出租车乘车概率预测的置信规则库推理方法[J]. 计算机科学与探索, 2015,9(8): 985-994.
    YANG L H, CAI Z L, HUANG Z X, et al. Belief rule-base inference methodology for predicting probability of taking taxi[J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(8): 985-994.
    [13]
    CHEN Y W, YANG J B, XU D L, et al. Inference analysis and adaptive training for belief rule based systems [J]. Expert Systems with Applications, 2011, 38(10): 12845-12860.
    [14]
    常瑞, 王红卫, 杨剑波. 基于梯度法与二分法的置信规则库参数训练方法 [J]. 系统工程, 2007: 287-291.
    CHANG R, WANG H W, YANG J B. An algorithm for training parameters in belief rule-bases based on the gradient and dichotomy methods [J]. Systems Engineering, 2007: 287-291.
    [15]
    苏群, 杨隆浩, 傅仰耿. 基于变速粒子群优化的置信规则库参数训练方法[J]. 计算机应用, 2014, 34(8): 2161-2165.
    SU Q, YANG L H, FU Y G. Parameter training approach based on variable particle swarm optimization for belief rule base[J]. Journal of Computer Application, 2014, 34(8): 2161-2165.
    [16]
    王韩杰, 杨隆浩, 傅仰耿, 等. 专家干预下置信规则库参数训练的差分进化算法[J]. 计算机科学, 2015, 42(5): 88-93.
    WANG H J, YANG L H ,FU Y G, et al. Differential evolutionary algorithm for parameter training of belief rule base under expert intervention[J]. Computer Science, 2015, 42(5):88-93.
    [17]
    LIU J, MARTINEZ L, CALZADA A, et al. A novel belief rule base representation, generation and its inference methodology [J]. Knowledge-Based Systems, 2013, 53: 129-141.
    [18]
    CALZADA A, LIU J, WANG H, et al. A new dynamic rule activation method for extended belief rule-based systems[J]. IEEE Transactions on Knowledge and data Engineering, 2015, 7(4): 880-888.
    [19]
    苏群, 杨隆浩, 傅仰耿, 等. 基于BK树的扩展置信规则库结构优化框架[J].计算机科学与探索, 2016, 10(2): 257-267.
    SU Q, YANG L H, FU Y G, et al. Structure optimization framework of extended belief rule base based on BK-tree[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(2): 257-267.
    [20]
    YANG L H, WANG Y M, SU Q, et al. Multi-attribute search framework for optimizing extended belief rule-based systems [J]. Information Science, 2016: 370-371.
    [21]
    FRANK A, ASUNCION A. UCI machine learning repository[EB/OL]. [2017-04-16]http://archive.ics.uci.edu/.
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