| 基于边界混合重采样的非平衡数据分类方法 |
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| 引用本文： | 侯贝贝,刘三阳,普事业.基于边界混合重采样的非平衡数据分类方法[J].计算机工程与应用,2020,56(1):46-52. |
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| 作者姓名： | 侯贝贝 刘三阳 普事业 |
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| 作者单位： | 西安电子科技大学 数学与统计学院，西安 710126 |
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| 基金项目： | 陕西省自然科学基金;国家自然科学基金 |
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| 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。
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| 关 键 词： | 支持k-离群度 重采样 边界点 非平衡数据分类 |
Imbalanced Data Classification Method Based on Boundary Mixed Resampling |
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| HOU Beibei,LIU Sanyang,PU Shiye.Imbalanced Data Classification Method Based on Boundary Mixed Resampling[J].Computer Engineering and Applications,2020,56(1):46-52. |
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| Authors: | HOU Beibei LIU Sanyang PU Shiye |
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| Affiliation: | School of Mathematics and Statistics, Xidian University, Xi’an 710126, China |
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| Abstract: | 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. |
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| Keywords: | k-outlier resampling boundary points imbalanced data classification |
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