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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/145946
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/145946


    Title: 監督式異常偵測與孿生神經網路
    Supervised Anomaly Detection using Siamese Neural Network
    Authors: 謝博丞
    Hsieh, Bo-Cheng
    Contributors: 周珮婷
    陳怡如

    Chou, Pei-Ting
    Chen, Yi-Ju

    謝博丞
    Hsieh, Bo-Cheng
    Keywords: 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
    Date: 2023
    Issue Date: 2023-07-06 17:05:40 (UTC+8)
    Abstract: 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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    Description: 碩士
    國立政治大學
    統計學系
    110354023
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110354023
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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