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题名:	Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor
作者:	周珮婷 Chou, Elizabeth P.;Hsieh, Bo-Cheng
贡献者:	統計系
关键词:	siamese neural network;anomaly detection;structured data;supervised learning
日期:	2025-03
上传时间:	2025-04-30 15:03:20 (UTC+8)
摘要:	Anomaly detection in structured data presents significant challenges, particularly in scenarios with extreme class imbalance. The Siamese Neural Network (SNN) is traditionally recognized for its ability to measure pairwise similarities, rather than being utilized as a feature extractor. However, in this study, we introduce a novel approach by leveraging the feature extraction capabilities of SNN, inspired by the powerful representation learning ability of neural networks. We integrate SNN with four different classifiers and the Synthetic Minority Over-sampling Technique (SMOTE) for supervised anomaly detection and evaluate its performance across five structured datasets under varying anomaly ratios. Our findings reveal that, when used as a feature extractor, SNN significantly enhances classification performance and demonstrates superior robustness compared to traditional anomaly detection methods, particularly under extreme class imbalance. These results highlight the potential of repurposing SNN beyond similarity learning, offering a scalable and effective feature extraction framework for anomaly detection in structured data applications.
關聯:	Mathematics, Vol.13, No.7, 1090
数据类型:	article
DOI 連結:	https://doi.org/10.3390/math13071090
DOI:	10.3390/math13071090
显示于类别:	[統計學系] 期刊論文

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