计算机应用研究2012，Vol.29Issue(3)：847-848,2.DOI:10.3969/j.issn.1001-3695.2012.03.012
剪枝与欠采样相结合的不平衡数据分类方法
Pruning and undersampling combination of imbalanced data classification method
摘要
Abstract
This paper proposed pruning and under-sampling combined approaches for selected the representative data as traiing data to improve the classification accuracy for minority class and investigated the effect of under-sampling methods in timbalanced class distribution environment. The experimental results show that the accuracy of algorithm of this paper compswith direct undersampling algorithm have increased, the most important is to significantly improve the g-means value. Espcially, the effect will be better on the imbalance rate of larger data sets.关键词
机器学习/不平衡数据集/剪枝技术/欠采样技术/交叉验证/合并分类器增强算法Key words
machine learning/ imbalanced data sets/ pruning techniques/ under-sampling/ cross-validation/ AdaBoost alg rithm分类
信息技术与安全科学引用本文复制引用
张健,方宏彬..剪枝与欠采样相结合的不平衡数据分类方法[J].计算机应用研究,2012,29(3):847-848,2.基金项目
国家自然科学基金资助项目(71071002) （71071002）
安徽省教育厅自然科学基金资助项目(05010428) （05010428）
安徽大学人才队伍建设项目 （）
安徽大学学术创新团队项目(KJTD001B) （KJTD001B）
