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研究生： 吳宥澄
Wu, You-Cheng
論文名稱： 結合條件生成對抗網路與對比學習進行過採樣之異常檢測法
Generative Oversampling with Conditional Generative Adversarial Network and Contrastive Learning for Anomaly Detection
指導教授： 李昇暾
Li, Sheng-Tun
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2024
畢業學年度： 112
語文別： 中文
論文頁數： 55
中文關鍵詞： 異常偵測 、不平衡分類 、對比學習 、生成對抗網路 、過採樣
外文關鍵詞： Anomaly detection, imbalanced classification, contrastive learning, Generative Adversarial Network, oversampling
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    • 異常偵測為數據分析領域中一個重要的研究議題，其目的在於辨別偏離正常情況的異常數據點或樣態，幾乎各行各業都有使用異常偵測技術的需求，包括金融、生產製造、醫療保健以及交通等。
    傳統的異常偵測通常仰賴專家判讀，在資料量不斷增長的今日是高成本且低效率的做法。近期由於技術創新，機器學習被廣泛應用至異常偵測，然而一般而言，正常樣本數會遠多於異常樣本數，導致分類器的表現可能會受到不平衡資料的影響，使偵測異常的能力受損。
    為克服異常偵測資料集普遍存在之不平衡特性，本研究試以對比學習 (Contrastive learning) 與條件生成對抗網路 (Conditional Generative Adversarial Network) 進行特徵學習與過採樣 (Oversampling)，再利用分類器判別異常，此方法將資料轉換為更容易區分之特徵，並改善分類器於訓練時過於關注正常類別樣本而忽略異常類別樣本之情形，以提升檢測異常之性能。

    Anomaly detection is a critical research topic in data analysis that aims to spot unusual data points or patterns that differ from normal behavior. Almost every industry requires anomaly detection technology, including finance, healthcare, manufacturing, transportation, and many others.
    Conventional anomaly detection methods often rely on manual interpretation, which has become costly and ineffective due to the increasing data volume. Thanks to technological innovations, machine learning-based systems have been generally utilized to detect anomalies. However, in most cases, the number of normal instances is much larger than the number of anomalous instances, so that the performance of a classifier may suffer from the imbalanced dataset, leading to impaired ability to detect anomalies.
    To address the problem of imbalanced datasets in anomaly detection, this research utilizes contrastive learning method and conditional Generative Adversarial Network (cGAN) for representation learning and oversampling. The attached classifier then performs the anomaly detection task. This method enhances the performance of anomaly detection by making features more separable and reducing the dominance of the normal class during training.

    摘要 i Abstract ii 致謝 iii Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 Chapter 2 Literature Review 4 2.1 Class Imbalance Issue of Anomaly Detection 4 2.2 Oversampling Techniques 5 2.3 Conditional Generative Adversarial Network 7 2.4 Contrastive Learning 9 2.5 Summary 12 Chapter 3 Methodology 13 3.1 Problem Definition 13 3.2 The Architecture 13 3.3 Training Process 14 3.3.1 Conditional Generative Adversarial Network Training 14 3.3.2 Initial Latent Vector Approximation 16 3.3.3 Contrastive Encoder Training 17 Chapter 4 Experiments 20 4.1 Datasets 20 4.2 Experimental Setup 21 4.2.1 Experimental Architecture 21 4.2.2 Competing Methods 22 4.2.3 Classifier Implementation 22 4.2.4 Hyperparameter Settings 23 4.3 Evaluation Metrics 24 4.4 Experimental Results 26 4.5 Ablation Study 36 4.6 Summary 37 Chapter 5 Conclusions 39 5.1 Conclusions and Contributions 39 5.2 Limitations and Future Works 40 References 42

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