基于边界混合重采样的非平衡数据分类方法  

Imbalanced Data Classification Method Based on Boundary Mixed Resampling

作　　者：侯贝贝 刘三阳 普事业 HOU Beibei;LIU Sanyang;PU Shiye(School of Mathematics and Statistics,Xidian University,Xi’an 710126,China)

机构地区：[1]西安电子科技大学数学与统计学院

出　　处：《计算机工程与应用》2020年第1期46-52,共7页Computer Engineering and Applications

基　　金：国家自然科学基金（No.61877046）;陕西省自然科学基金（No.2017JM1001）

摘　　要：在非平衡数据分类问题中,为了合成有价值的新样本和删除无影响的原样本,提出一种基于边界混合重采样的非平衡数据分类算法。该算法首先引入支持k-离群度概念,找出数据集中的边界点集和非边界点集;利用改进的SMOTE算法将少数类中的边界点作为目标样本合成新的点集,同时对多数类中的非边界点采用基于距离的欠采样算法,以此达到类之间的平衡。通过实验结果对比表明了该算法在保证G-mean值较优的前提下,一定程度上提高了少数类的分类精度。In the problem of imbalanced data classification,aiming to synthesize valuable new samples and delete the original samples without any influence,a novel imbalanced data classification method based on boundary mixed resampling is proposed.Firstly,the concept of k-outlier is introduced to find out the boundary and non-boundary samples and then deal with them in different ways.The minority samples in boundary are taken as the target points to synthesize new sample points while the non-boundary majority ones are under sampled based on distance to achieve a basic balance of samples.By comparing the experimental results,it shows that the proposed algorithm achieves a better classification performance on the classification accuracy of minority samples to some extent on the premise of ensuring a better G-mean value.

关 键 词：支持k-离群度 重采样 边界点 非平衡数据分类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]