%0 Journal Article
%T Pruning and undersampling combination of imbalanced data classification method<br>剪枝与欠采样相结合的不平衡数据分类方法*
%A ZHANG Jian
%A FANG Hong-bin
%A <br>张健
%A 方宏彬
%J 计算机应用研究
%D 2012
%I 
%X This paper proposed pruning and under-sampling combined approaches for selected the representative data as training data to improve the classification accuracy for minority class and investigated the effect of under-sampling methods in the imbalanced class distribution environment. The experimental results show that the accuracy of algorithm of this paper compare with direct undersampling algorithm have increased, the most important is to significantly improve the g-means value. Especially, the effect will be better on the imbalance rate of larger data sets.
%K machine learning
%K imbalanced data sets
%K pruning techniques
%K under-sampling
%K cross-validation
%K AdaBoost algorithm<br>机器学习
%K 不平衡数据集
%K 剪枝技术
%K 欠采样技术
%K 交叉验证
%K 合并分类器增强算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F950C016BC33E90171E7DC99C7116841&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=38B194292C032A66&sid=BCCCE1B88B87184D&eid=51E4ADE955550A0C&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11