%0 Journal Article
%T Minimum distance classifier ensemble based on adaptive distance metric
基于自适应距离度量的最小距离分类器集成
%A GUO Ya-qin
%A WANG Zheng-qun
%A LE Xiao-rong
%A WANG Xiang-dong
%A
郭亚琴
%A 王正群
%A 乐晓容
%A 王向东
%J 计算机应用
%D 2006
%I
%X A minimum distance classifier ensemble method based on adaptive distance metric was proposed. The training method of component classifier was given. Some training subsets were obtained via bootstrap technique, then the model about adaptive distance metric with the training subset was established. Each component classifier was trained independently using the model, then some component classifiers were obtained. After that, they were collected to make a decision according to the majority voting. Experiment results on UCI standard database show that the proposed ensemble method based on adaptive distance metric for minimum distance classifier is effective, and it is superior to other methods in classification performance.
%K adaptive distance metric
%K minimum distance classifier
%K classifier ensemble
%K component classifier
%K majority voting
自适应距离度量
%K 最小距离分类器
%K 分类器集成
%K 个体分类器
%K 多数投票法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=88AB265C9E2CB00C&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=DF92D298D3FF1E6E&sid=10AEA069F4433410&eid=3183893ED5218CD5&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=11