%0 Journal Article
%T Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost<br>基于AdaBoost的改进模糊分类规则集成学习
%A Fang Min
%A Wang Bao-shu
%A <br>方敏
%A 王宝树
%J 电子与信息学报
%D 2005
%I 
%X 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.
%K Fuzzy classification rule
%K Adaptive Boosting (AdaBoost) algorithm
%K Classifiers ensemble<br>模糊分类规则
%K AdaBoost算法
%K 分类器集成
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=817FC6A9638BF4E4&yid=2DD7160C83D0ACED&vid=DB817633AA4F79B9&iid=94C357A881DFC066&sid=9EB9AF946ABE60ED&eid=2C8B50BA95995EA2&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=7