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基于AdaBoost的改进模糊分类规则集成学习 被引量：2

Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost
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摘要 基于集成学习提出了一种新的模糊分类规则的产生算法。将分类规则的前件、后件模糊化,在自适应提升(Adaptive Boosting,AdaBoost)算法的迭代中,调整训练实例的分布,利用遗传算法产生模糊分类规则。并在规则学习的适应度函数中引入训练实例的分布,使得模糊分类规则在产生阶段就考虑相互之间的协作,产生具有互补性的分类规则集。从而改善了模糊分类规则的整体识别能力,提高了分类识别精度。 A new learning algorithm of fuzzy classification rules is presented based on ensemble learning algorithm. By tuning the distribution of training instances during each AdaBoost iterative training, the classification rules with fuzzy antecedent and consequent are produced with genetic algorithm. The distribution of training instances participate in computing of the fitness function and the collaboration of rules which are complementary is taken into account during rules producing, so that the classification error rate is reduced and performance of the classification based on the fuzzy rules is improved.
作者 方敏 王宝树
机构地区 西安电子科技大学综合业务网国家重点实验室 西安电子科技大学计算机学院
出处 《电子与信息学报》 EI CSCD 北大核心 2005年第5期835-837,共3页 Journal of Electronics & Information Technology
基金 国家部级基金(413150801)综合业务网国家重点实验室开放基金(ISN6-7)资助课题
关键词 模糊分类规则 ADABOOST算法 分类器集成 Fuzzy classification rule, Adaptive Boosting (AdaBoost) algorithm, Classifiers ensemble
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献7

  • 1Cordon O, del Jesus M J. Genetic learning of fuzzy classification systems cooperating with fuzzy reasoning methods [J].International Journal of Intelligent Systems, 1998, 13(10/11):1025- 1053.
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同被引文献28

  • 1Bing Liu, Yiming Ma, Ching Kian Wong. Improving an association rule based classifier[C]. Proc of the4th European Conf on Principles of Data Mining and Knowledge Discovery. Lyon, 2000: 504-509.
  • 2Alberto Fem~indez, Salvador Garcfa, Marfa Jos6 del Jesusb, et al. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets[J]. Fuzzy Sets and Systems, 2008, 159(18): 2378- 2398.
  • 3Alberto Fernandez, Maria Jos6 del Jesus, Francisco Herrera. On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets[J]. Expert Systems with Applications, 2009, 36(6): 9805-9812.
  • 4Batista G, Prati R C, Monard M C. A study of the behavior of several methods for balancing machine learning training data[J]. SIGKDD Explorations, 2004, 6(1): 20-29.
  • 5Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. J of Computer and System Sciences, 1997, 55(1): 119-139.
  • 6Haibo He, Edwardo. A garcia learning from imbalancedData[J]. IEEE Trans on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
  • 7Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou. Exploratory underSampling for class-imbalance learning[J]. IEEE Trans on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39(2): 539-549.
  • 8Chawla N V, Lazarevic A, Hall L O, et al. SMOTEBoost: Improving prediction of the minority class in boosting[C]. Proc of the 7th European Conf on Principles and Practice of Knowledge Discovery in Databases. Dubrovnik, 2003: 107-119.
  • 9Guo H, Viktor H L. Learning from imbalanced data sets with boosting and data generation: The databoost-IM approach[J]. SIGKDD Explorations, 2004, 6(1): 30-39.
  • 10Sun Y, Kamel M S, Wong A K C, et al. Cost-sensitive boosting for classification of imbalanced data[J]. Pattern Recognition, 2007, 40(12): 3358-3378.

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电子与信息学报
2005年 第5期

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