%0 Journal Article
%T Classifier optimization method using niche genetic algorithm<br>基于小生境遗传算法的分类优化方法
%A LI Jun-ying
%A LOU Xiao-jun
%A <br>李隽颖
%A 楼晓俊
%J 计算机应用研究
%D 2012
%I 
%X According to multi-classify problem, the multi-classes classifier constructed by binary classes classifier are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very difficult. Hyper-sphere support vector machine (HSSVM) can be extended to solve this multi-classification problem. Each category data trains only one HSSVM, the sample space is divided by multiple optimal hyper-spheres. In order to improve the performance of classifier, this paper used improved crowding niche genetic algorithm (ICNGA) to select features, chose optimal feature subset for different target classes. Using UCI data set of numerical experiment shows that the classifiers have a higher accuracy if ICNGA has been used for feature selection, especially the sample data has a large number of categories or feature vectors.
%K genetic algorithm(GA)
%K crowding niche technology
%K hyper-sphere support vector machine(HSSVM)
%K feature selection<br>遗传算法
%K 排挤小生境技术
%K 超球支持向量机
%K 特征选择
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC5B920E24A3CF104A9&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=65A936AA46C30FB3&eid=D227C2A170C3A3E4&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=16