Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
3055-3060

Generation mechanism for minority samples with hypercuboid constraints

He Zuowei1
Tao Jiaqing1
Leng Qiangkui2
Zhai Junchang1
Meng Xiangfu2
1. College of Information Science & Technology, Bohai University, Jinzhou Liaoning 121013, China
2. School of Electronics & Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0099

Abstract

Synthetic minority oversampling technology(SMOTE) is one of the effective methods to solve the class-imbalanced problem. However, the linear interpolation mechanism of SMOTE restricts the synthesized samples to the connecting line of the original samples, resulting in a lack of diversity for new samples, and may generate noisy samples when this line passes through the majority class region. In response to the above issues, this paper proposed a generation mechanism for minority samples with hypercuboid constraints. This mechanism constructed a hypercuboid as the generation region of new samples instead of linear interpolation, thereby increasing the variability between the synthesized samples and the original samples. Then, it detected whether there were majority samples in the hypercuboid to determine whether to adjust the hypercuboid, which aimed at preventing the new samples into the region of the majority class. This paper integrated the proposed mechanism into three oversampling methods, i. e., SMOTE, Borderline-SMOTE and ADASYN, by using it to replace linear interpolation, and then experimentally evaluated the integrated method on 11 benchmark datasets from KEEL. The results show that compared to the original method, the integrated method can help the classifier to obtain higher F1 and comparable G-mean. It verifies that the hypercuboid generation mechanism can significantly improve the classifier's ability to recognize minority samples, and meanwhile the majority samples are also taken into account.

Foundation Support

国家自然科学基金资助项目(61602056、61772249)
辽宁省自然科学基金资助项目(2019-ZD-0493)
辽宁省教育厅科研项目(LQ2019012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0099
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Algorithm Research & Explore
Pages: 3055-3060
Serial Number: 1001-3695(2022)10-026-3055-06

Publish History

[2022-05-13] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

贺作伟, 陶佳晴, 冷强奎, 等. 带有超长方体约束的少数类样本生成机制 [J]. 计算机应用研究, 2022, 39 (10): 3055-3060. (He Zuowei, Tao Jiaqing, Leng Qiangkui, et al. Generation mechanism for minority samples with hypercuboid constraints [J]. Application Research of Computers, 2022, 39 (10): 3055-3060. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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