基于空间近邻关系的非平衡数据重采样算法.pdf

工程科学学报，第 43 卷，第 6 期：862−869，2021 年 6 月
Chinese Journal of Engineering, Vol. 43, No. 6: 862−869, June 2021
neighbor relationship of sample space was proposed. This method
first evaluated the security level according to the spatial neighbor relations of minority samples and oversampled them through the
synthetic minority oversampling technique guided by their security level. Then, the local density of majority samples was calculated
according to their spatial neighbor relation to undersample the majority samples in a sample-intensive area. By the above two means, the
data set can be balanced and the data size can be controlled to prevent overfitting to realize the classification equalization of the two
categories. The training set and test set were generated via the method of 5 × 10 fold cross validation. After resampling the training set,
the kernel extreme learning machine (KELM) was used as the classifier for training, and the test set was used for verification. The
experimental results on a UCI imbalanced data set and measured circuit fault diagnosis data show that the proposed method is superior to
other resampling algorithms.
收稿日期: 2020−04−05
基金项目: 军内科研项目“新一代航空电子装备测试关键技术研究”资助项目（4172122113R）
万方数据李睿峰等： 基于空间近邻关系的非平衡数据重采样算法 · 863 ·
KEY WORDS    imbalanced data；neighbor relationship；