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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/156774

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/156774

Title:	Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor
Authors:	周珮婷 Chou, Elizabeth P.;Hsieh, Bo-Cheng
Contributors:	統計系
Keywords:	siamese neural network;anomaly detection;structured data;supervised learning
Date:	2025-03
Issue Date:	2025-04-30 15:03:20 (UTC+8)
Abstract:	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.
Relation:	Mathematics, Vol.13, No.7, 1090
Data Type:	article
DOI link:	https://doi.org/10.3390/math13071090
DOI:	10.3390/math13071090
Appears in Collections:	[Department of Statistics] Periodical Articles

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