A Novel Borderline Over-Sampling Method Based on KNN and Deep Gaussian Mixture Model for Imbalanced Data

被引：0

作者：

Zhang H. ^{[1
,2
]}

Xiao H. ^{[1
,3
]}

Yi C. ^{[1
,3
]}

Yuan R. ^{[1
,3
]}

机构：

[1] Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan

[2] Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan

[3] Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan

来源：

Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 05期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Deep Gaussian Mixture Model; Imbalanced Data; Over-Sampling;

D O I：

10.11925/infotech.2096-3467.2022.0609

中图分类号：

学科分类号：

摘要：

[Objective] This paper proposes a borderline oversampling method based on the k-nearest neighbor algorithm (KNN) and Deep Gaussian Mixture Model (DGMM) to address the classifier bias due to data imbalance. [Methods] Firstly, we used the KNN algorithm to obtain the borderline minority samples in the training set. Secondly, we constructed a DGMM for the minority samples. Next, we applied the DGMM in reverse to generate the oversampling samples that conform to the distribution characteristics of the borderline minority samples. Finally, we used the three sigma guidelines to remove noise samples. We repeated the process until no outlier samples were generated. [Results] The proposed method improved the AUC and G-mean up to 8.62% and 12.99%, respectively. The corresponding average increased by 3.51% and 4.93%. [Limitations] The parameter optimization method for DGMM needs further improvement. [Conclusions] The proposed method can better address the problem of imbalanced data. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.

引用

页码：116 / 122

页数：6

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