Advanced Search
Volume 39 Issue 5
May  2017
Turn off MathJax
Article Contents
XU Mingliang, WANG Shitong. Extracting Fuzzy Rules from the Maximum Ball Containing the Homogeneous Data[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1130-1135. doi: 10.11999/JEIT160779
Citation: XU Mingliang, WANG Shitong. Extracting Fuzzy Rules from the Maximum Ball Containing the Homogeneous Data[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1130-1135. doi: 10.11999/JEIT160779

Extracting Fuzzy Rules from the Maximum Ball Containing the Homogeneous Data

doi: 10.11999/JEIT160779
Funds:

The National Natural Science Foundation of China (61170122, 61202311, 61272210), The Natural Science Foundation of Jiangsu Province (BK2012552), The Qing Lan Project of Jiangsu Province (2014)

  • Received Date: 2016-07-22
  • Rev Recd Date: 2017-01-09
  • Publish Date: 2017-05-19
  • In order to improve the interpretability and effectiveness of the fuzzy classifier rules, this paper presents a new method to extract the fuzzy rules based on the maximum ball only containing the homogeneous data. At first, every sample constructs a maximum ball in the light of the shortest distance to heterogeneous samples. Then those balls are reduced according to the relation of inclusion and the unique among the samples that the ball encloses. Then the fuzzy rules are constructed with the reserved balls. The parameters learning of the antecedent part of the classifier are based on the minimization of the weight misclassification quadratic error and resolved with the conjugate gradient algorithm. The experiments on 12 benchmark datasets with 10 folds are performed to demonstrate the validity of the classifier.
  • loading
  • HARANDI F A and DERHAMI V. A reinforcement learning algorithm for adjusting antecedent parameters and weights of fuzzy rules in a fuzzy classifier[J]. Journal of Intelligent Fuzzy Systems, 2016, 30(4): 2339-2347. doi: 10.3233/IFS- 152004.
    JAMALABADI H, NASROLLAHI H, ALIZADEH S, et al. Competitive interaction reasoning: A bio-inspired reasoning method for fuzzy rule based classification systems[J]. Information Sciences, 2016, 352: 35-47. doi: 10.1016/ j.ins.2016.02.052.
    CINTRA M E, CAMARGO H A, and MONARD M C. Genetic generation of fuzzy systems with rule extraction using formal concept analysis[J]. Information Sciences, 2016, 349: 199-215. doi: 10.1016/j.ins.2016.02.026
    POURPANAHA F, LIM C P, and MOHAMAD SALEHA J. A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction[J]. Expert Systems With Applications, 2016, 49: 74-85. doi: 10.1016/j.eswa. 2015.11.009.
    李继东, 张学杰. 基于遗传算法的多维模糊分类器构造的研究[J]. 软件学报, 2005, 16(5): 779-785.
    LI J D and ZHANG X J. Research on the construction of fuzzy classifier system for multidimensional pattern classification using genetic algorithms[J]. Journal of Software, 2005, 16(5): 779-785.
    RUDZINSKI F. A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers[J]. Applied Soft Computing, 2016, 38: 118-133. doi: 10.1016/ j.asoc.2015.09.038.
    MARIAN B G and RUDZINSKI F. A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability[J]. Applied Soft Computing, 2016, 40: 206-220. doi: 10.1016/j.asoc.2015. 11.037.
    SHANGHOOSHABAD A M and ABADEH M S. Robust, interpretable and high quality fuzzy rule discovery using krill herd algorithm[J]. Journal of Intelligent and Fuzzy Systems, 2016, 30(3): 1601-1612. doi: 10.3233/IFS-151867
    GARCA-GALN S, PARDO P R, and MUNOZ EXPSITO J E. Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures[J]. Applied Soft Computing, 2015, 29: 424-435. doi: 10.1016/j.asoc.2014.11.064.
    WU Jue, YANG Lei, LI Tianrui, et al. Rule-based fuzzy classifier based on quantum ant optimization algorithm[J]. Journal of Intelligent Fuzzy Systems, 2015, 29 (6): 2365-2371. doi: 10.3233/IFS-151935.
    MAHDIZADEH M and EFTEKHARI M. Generating fuzzy rule base classifier for highly imbalanced datasets using a hybrid of evolutionary algorithms and subtractive clustering[J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(6): 3033-3046. doi: 10.3233/IFS-141261.
    邢宗义, 张永, 侯远龙, 等. 基于模糊聚类和遗传方法的具备解释性和精确性的模糊分类系统设计[J]. 电子学报, 2006, 34(1): 83-88.
    XING Zongyi, ZHANG Yong, HOU Yuanlong, et al. Design of interpretable and precise fuzzy classification system based on fuzzy clustering and genetic algorithm[J]. Acta Electronica Sinica, 2006, 34(1): 83-88.
    王莉, 周献中, 李华雄. 基于决策粗糙集的模糊分类模型[J]. 信息与控制, 2014, 43(1): 24-29. doi: 10.3724/SP.J.1219.2014. 00024.
    WANG Li, ZHOU Xianzhong, and LI Huaxiong. Fuzzy classification model based on decision-theoretic rough set[J]. Information and Control, 2014, 43(1): 24-29. doi: 10. 3724 /SP.J.1219.2014.00024.
    JACK M L. 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. doi: 10.1109/TFUZZ.2014.2327995
    LESKI J M. Iteratively reweighted least squares classifier and is - and -regularized kernel versions[J]. Bulletin of the Polish Academy of Sciences: Technical Sciences, 2010, 58(1): 171-182. doi: 10.2478/v10175-010-0018-2.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1024) PDF downloads(315) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return