政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/145946

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/145946

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题名:	監督式異常偵測與孿生神經網路 Supervised Anomaly Detection using Siamese Neural Network
作者:	謝博丞 Hsieh, Bo-Cheng
贡献者:	周珮婷陳怡如 Chou, Pei-Ting Chen, Yi-Ju 謝博丞 Hsieh, Bo-Cheng
关键词:	異常偵測孿生神經網路機器學習監督式學習結構化資料 Anomaly Detection Siamese Neural Network Machine Learning Supervised Learning Structured Data
日期:	2023
上传时间:	2023-07-06 17:05:40 (UTC+8)
摘要:	孿生神經網路是一種基於度量的小樣本學習 (Few-Shot Learning)，其特殊的訓練方式與神經網路架構，於少量資料情境可有效進行特徵提取，提升分類器的準確度。孿生神經網路在電腦視覺、自然語言處理領域已被廣為使用作為非結構化資料的特徵提取方法，但鮮少有研究將其應用於結構化資料，並與其他演算法進行比較。本研究利用孿生神經網路與其他四種分類器以及重複採樣技術 SMOTE 組合，共 9 種演算法組合，對 5 筆資料進行監督式異常偵測，並測試異常比例對各種演算法的影響。研究結果發現孿生神經路於結構化資料表現優秀，且相對於其他演算法更不受資料異常比例影響。 The Siamese neural network is a metric-based few-shot learning method that can effectively extract features from a small amount of data and improve classifier ac- curacy through its special training method and neural network architecture. While it has been widely used as a feature extraction method for unstructured data in com- puter vision and natural language processing, few studies have compared it with other algorithms in structured data applications. In this study, we combined nine algo- rithm combinations using a siamese neural network, four different classifiers, and SMOTE for supervised anomaly detection with 5 datasets. We also tested the effect of anomaly ratios on various algorithms. The results showed that the Siamese neural network performed well in structured data and was less affected by data anomalies than other algorithms.
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Anomaly detection using siamese network with attention mechanism for few-shot learning. Applied Artificial Intelligence, 36(1):2094885. Tantithamthavorn, C., Hassan, A. E., and Matsumoto, K. (2018). The impact of class re- balancing techniques on the performance and interpretation of defect prediction models. IEEE Transactions on Software Engineering, 46(11):1200–1219.
描述:	碩士國立政治大學統計學系 110354023
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110354023
数据类型:	thesis
显示于类别:	[統計學系] 學位論文

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