作　　者： ; ;

机构地区： 安徽大学

出　　处： 《计算机应用研究》 2012年第3期847-848,共2页

摘　　要： 通过剪枝技术与欠采样技术相结合来选择合适数据,以提高少数类分类精度,研究欠采样技术在不平衡数据集环境下的影响。结果表明,与直接欠采样算法相比,本文算法不仅在accuracy值上有所提高,更重要的是大大改善了g-means值,特别是对非平衡率较大的数据集效果会更好。 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.

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

领　　域： [自动化与计算机技术] [自动化与计算机技术]