Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 12
3605-3613

Fraud detection model generalization performance improvement and interpretability study based on ADASYN-SFS-RF

Wang Wanmin
Zhi Luping
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0237

Abstract

Aiming at the problems of various forms, hidden operations, and extremely unbalanced data distribution of fraud in the industry, this paper adopted the ADASYN algorithm to adaptively move the classification decision boundary to difficult instances to achieve data augmentation, to solve the over-fitting problem caused by unbalanced data. It used the sequence forward search strategy algorithm based on the random forest to filter out the optimal feature subset to detect fraud, reduced the impact of noise data added by the ADASYN algorithm on the determination of classification boundary, constructed a fraud detection model, and used LIME to make local interpretation of the model detection results to improve the use of the model. The experiments show that the model can better overcome the defects of traditional fraud detection models in misclassifying most classes of samples, and help to improve the efficiency of transaction fraud identification in the industry. At the same time, the random samples detected by the model are effectively analyzed through LIME, which is convenient for decision-makers to make empirical analyses on the detection results of the algorithm model and plays an obvious early warning and decision-making reference value.

Foundation Support

国家自然科学基金项目(71801150)
上海市人民政府决策咨询研究项目(2022-Z-J07)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0237
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 12
Section: Algorithm Research & Explore
Pages: 3605-3613
Serial Number: 1001-3695(2022)12-012-3605-09

Publish History

[2022-07-26] Accepted Paper
[2022-12-05] Printed Article

Cite This Article

汪万敏, 智路平. 基于ADASYN-SFS-RF的欺诈检测模型泛化性能提升及可解释性研究 [J]. 计算机应用研究, 2022, 39 (12): 3605-3613. (Wang Wanmin, Zhi Luping. Fraud detection model generalization performance improvement and interpretability study based on ADASYN-SFS-RF [J]. Application Research of Computers, 2022, 39 (12): 3605-3613. )

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  • 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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