基于AdaBoost的改进模糊分类规则集成学习  

Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost

作　　者：方敏[1] 王宝树[2] 

机构地区：[1]西安电子科技大学综合业务网国家重点实验室,西安710071 [2]西安电子科技大学计算机学院,西安710071

出　　处：《电子与信息学报》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

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.

关 键 词：模糊分类规则 ADABOOST算法 分类器集成 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]