%0 Journal Article
%T Modified differential evolution algorithm for semi-supervised fuzzy clustering<br>改进微分进化算法的半监督模糊聚类
%A ZHANG Song-shun
%A LI Chao-feng
%A WU Xiao-jun
%A GAO Cui-fang
%A <br>张松顺
%A 李朝锋
%A 吴小俊
%A 高翠芳
%J 计算机应用
%D 2009
%I 
%X Through studying and classifying labeled and unlabeled data, this paper proposed a modified differential evolution algorithm for semi-supervised fuzzy clustering. Firstly, a small part of data was labeled from the whole dataset, and then these labeled data were used to guide the evolution process to partition unlabeled data. The modified algorithm introduces inertia-weighted coefficient by considering inertia-weighted idea of particle swarm algorithm, which keeps diversity of individual at early stages and quickens convergent speed at later stages, and at the same time improves the performance of the algorithm. The experimental results for remote sensing data indicate that the proposed approach can improve classification accuracy.
%K fuzzy cluster
%K labeled data
%K unlabeled data
%K differential evolution algorithm
%K semi-supervised learning<br>模糊聚类
%K 标示数据
%K 未标示数据
%K 微分进化算法
%K 半监督学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=5693C22399C3C030D54E063ECFF3583D&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=E158A972A605785F&sid=9A7C41A6BCE530C0&eid=1EA033F7E0CC5F25&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=8