%0 Journal Article
%T Research on the Construction of Fuzzy Classifier System for Multidimensional Pattern Classification Using Genetic Algorithms<br>基于遗传算法的多维模糊分类器构造的研究
%A LI Ji-dong
%A ZHANG Xue-Jie
%A <br>李继东
%A 张学杰
%J 软件学报
%D 2005
%I 
%X This paper discusses the application and performance of multidimensional pattern classification problems using Michigan approach based on fuzzy genetics-based machine learning mechanism, and proposes a new approach. In the approach, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to it. The approach not only retrieves fuzzy if-then rules, but also tunes the membership functions of each dimension, meanwhile the selection mechanism based on the similarity of individuals is involved to reduce the high selective pressure, keep the diversity of population, and avoid the premature convergence problem consequently. Finally the experiments prove that the approach has a better correct classification rate and a better adaptability on multidimensional pattern classification problems.
%K fuzzy genetic-based machine learning mechanism
%K Michigan approach
%K fuzzy classifier
%K premature convergence
%K elist selection
%K selection mechanism based on similarity<br>模糊遗传学习机制
%K 密歇根方法
%K 模糊分类器
%K 早熟收敛
%K 精英选择
%K 基于相似性的选择
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=E36CE1EAA0BBADD5&yid=2DD7160C83D0ACED&vid=7801E6FC5AE9020C&iid=94C357A881DFC066&sid=EEAFC972BAD75B1F&eid=A1BB529A18D3A83E&journal_id=1000-9825&journal_name=软件学报&referenced_num=1&reference_num=18