ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Belief rule based inference methodology for classification based on differential evolution algorithm

  • 1.

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

  • 2.

    Decision Sciences Institute, Fuzhou University, Fuzhou 350116, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.09.008
  • Received Date: 01 March 2016
  • Accepted Date: 17 September 2016
  • Rev Recd Date: 17 September 2016
  • Publish Date: 30 September 2016
    Abstract

  • Abstract

    A new method, based on belief rule base(BRB) was proposed for constructing a classification system with high performance for classification problems. The existing belief rule base classification system (BRBCS) is flawed because its classification accuracy is limited to the partition number, the parameter training method needs the number of rules given in advance, and the reasoning process does not reflect the correlation between characteristics and results. The belief rule base inference method was thus proposed for classification based on the differential evolutionary algorithm (DEBRM) to solve the classification problems. The proposed method consists of two procedures: belief rule base classification system (BRBCS) and parameter training method. The new method first introduced the construction strategy of BRBCS to determine the number of rules. Then, belief reasoning method was adopted as the inference engine. Finally, the training model for classification which is combined with differential evolutionary algorithm was built. In the experiment analysis, the effectiveness of the method was validated by comparing it with the existing parameter training method, and the rationality of parameters training in comparison of other belief rule base methods for different number of partitions. The classification results show that the proposed method is effective and reasonable.

    Abstract

    A new method, based on belief rule base(BRB) was proposed for constructing a classification system with high performance for classification problems. The existing belief rule base classification system (BRBCS) is flawed because its classification accuracy is limited to the partition number, the parameter training method needs the number of rules given in advance, and the reasoning process does not reflect the correlation between characteristics and results. The belief rule base inference method was thus proposed for classification based on the differential evolutionary algorithm (DEBRM) to solve the classification problems. The proposed method consists of two procedures: belief rule base classification system (BRBCS) and parameter training method. The new method first introduced the construction strategy of BRBCS to determine the number of rules. Then, belief reasoning method was adopted as the inference engine. Finally, the training model for classification which is combined with differential evolutionary algorithm was built. In the experiment analysis, the effectiveness of the method was validated by comparing it with the existing parameter training method, and the rationality of parameters training in comparison of other belief rule base methods for different number of partitions. The classification results show that the proposed method is effective and reasonable.
    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    崔彩霞.智能分类方法[M].北京：气象出版社，2009: 1-216.
    [2]
    CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning，1995，20(3): 273-297.
    [3]
    PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences，1982，11(5): 341-356.
    [4]
    ZADEH L A. Fuzzy sets [J]. Information and Control，1965，8(3): 338-353.
    [5]
    RISH I. An empirical study of the naive Bayes classifier[J]. Journal of Universal Computer Science, 2001，1(2): 41-46.
    [6]
    COVER T M, HART P E. Nearest neighbor pattern classification [J]. IEEE Transactions on Information Theory，1967，13(1)：21-27.
    [7]
    DASARATHY B V. Nearest neighbor(NN) norms [A]// NN Pattern Classification Techniques. IEEE Computer Society，1991, 9: 1-30.
    [8]
    GUO N R, LI T H S. Construction of a neuron-fuzzy classification model based on feature-extraction approach [J]. Expert Systems with Applications，2011, 38(1): 682-691.
    [9]
    CHI Z, YAN H, PHAM T. Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition[M]. Singapore：World Scientific Publishing，2014.
    [10]
    ISHIBUCHI H, NOZAKI K, TANAKA H. Distributed representation of fuzzy rules and its application to pattern classification [J]. Fuzzy Sets and Systems，1992，52(1)：21-32.
    [11]
    SUN C T. Rule-base structure identification in an adaptive-network based fuzzy inference system [J]. IEEE Transactions on Fuzzy Systems，1994，2(1)：64-73.
    [12]
    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，2006，36(2)：266-285.
    [13]
    JIAO L M, PAN Q, DENOEUX T, et al. Belief rule-based classification system: extension of FRBCS in belief functions framework [J]. Information Sciences，2015，309：26-49.
    [14]
    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.
    [15]
    CHANG L L, ZHOU Z H, YOU Y, et al. Belief rule based expert system for classification problems [J]. Information Sciences，2015，336：75-91.
    [16]
    SUN R. Robust reasoning: Integrating rule-based and similarity-based reasoning [J]. Artificial Intelligence，1995，75(2)：241-295.
    [17]
    WANG Y M, YANG J B, XU D L. Environmental impact assessment using the evidential reasoning approach [J]. European Journal of Operational Research，2006，174(3)：1885-1913.
    [18]
    NOZAKI K, ISHIBUCHI H, TANAKA H. Adaptive fuzzy rule-based classification systems [J]. IEEE Transactions on Fuzzy Systems，1996，4(3)：238-250.
    [19]
    JOUSSELME A L, GRENIER D, BOSS . A new distance between two bodies of evidence[J]. Information Fusion，2001，2(2)：91-101.
    [20]
    FALLAHNEZHAD M, MORADI M H, ZAFERANLOUEI S. A hybrid higher order neural classifier for handling classification problems [J]. Expert Systems with Applications，2011，38(1)：386-393.)
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [1]
    崔彩霞.智能分类方法[M].北京：气象出版社，2009: 1-216.
    [2]
    CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning，1995，20(3): 273-297.
    [3]
    PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences，1982，11(5): 341-356.
    [4]
    ZADEH L A. Fuzzy sets [J]. Information and Control，1965，8(3): 338-353.
    [5]
    RISH I. An empirical study of the naive Bayes classifier[J]. Journal of Universal Computer Science, 2001，1(2): 41-46.
    [6]
    COVER T M, HART P E. Nearest neighbor pattern classification [J]. IEEE Transactions on Information Theory，1967，13(1)：21-27.
    [7]
    DASARATHY B V. Nearest neighbor(NN) norms [A]// NN Pattern Classification Techniques. IEEE Computer Society，1991, 9: 1-30.
    [8]
    GUO N R, LI T H S. Construction of a neuron-fuzzy classification model based on feature-extraction approach [J]. Expert Systems with Applications，2011, 38(1): 682-691.
    [9]
    CHI Z, YAN H, PHAM T. Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition[M]. Singapore：World Scientific Publishing，2014.
    [10]
    ISHIBUCHI H, NOZAKI K, TANAKA H. Distributed representation of fuzzy rules and its application to pattern classification [J]. Fuzzy Sets and Systems，1992，52(1)：21-32.
    [11]
    SUN C T. Rule-base structure identification in an adaptive-network based fuzzy inference system [J]. IEEE Transactions on Fuzzy Systems，1994，2(1)：64-73.
    [12]
    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，2006，36(2)：266-285.
    [13]
    JIAO L M, PAN Q, DENOEUX T, et al. Belief rule-based classification system: extension of FRBCS in belief functions framework [J]. Information Sciences，2015，309：26-49.
    [14]
    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.
    [15]
    CHANG L L, ZHOU Z H, YOU Y, et al. Belief rule based expert system for classification problems [J]. Information Sciences，2015，336：75-91.
    [16]
    SUN R. Robust reasoning: Integrating rule-based and similarity-based reasoning [J]. Artificial Intelligence，1995，75(2)：241-295.
    [17]
    WANG Y M, YANG J B, XU D L. Environmental impact assessment using the evidential reasoning approach [J]. European Journal of Operational Research，2006，174(3)：1885-1913.
    [18]
    NOZAKI K, ISHIBUCHI H, TANAKA H. Adaptive fuzzy rule-based classification systems [J]. IEEE Transactions on Fuzzy Systems，1996，4(3)：238-250.
    [19]
    JOUSSELME A L, GRENIER D, BOSS . A new distance between two bodies of evidence[J]. Information Fusion，2001，2(2)：91-101.
    [20]
    FALLAHNEZHAD M, MORADI M H, ZAFERANLOUEI S. A hybrid higher order neural classifier for handling classification problems [J]. Expert Systems with Applications，2011，38(1)：386-393.)
    Recommended articles
    TrendMD
    Volume 46 Issue 9 page: 764-773
    Cover

    Article Metrics

    Article views (30) PDF downloads(70)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return