異常偵測是機器學習和數據分析領域的重要挑戰之一，目前在實務上多數應用於欺詐偵測、網絡安全和故障診斷等不同領域。
首先，本研究探討各種異常偵測方法的運作原理、優點和缺點。例如，One-Class SVM適用於高維度數據，但需要仔細選擇kernal function和參數。Gaussian Mixture Model能夠擬合複雜的資料分佈，但需要大量的參數估計。
接著，本研究比較分析了六種不同的異常偵測技術，分別是One-Class SVM, Gaussian Mixture Model, Autoencoder, Isolation Forest, Local Outlier Factor，以及Ensemble Voting前五種方法。並將六種模型應用在五個不同的數據集上進行了實證實驗，以F1-score和Balanced Accuracy，評估每種模型方法在不同數據上的表現。
最後，研究結果顯示，Isolation Forest在特定某些數據集上表現出相當的性能，但是Ensemble Voting的模型在每個數據集上皆表現優異。

Anomaly detection is one of the significant challenges in the fields of machine learning and data analysis. It is primarily applied in various practical domains like fraud detection, cybersecurity, and fault diagnosis.
Initially, this study explores the operational principles, advantages, and disadvantages of various anomaly detection methods. For instance, the One-Class SVM is suitable for high-dimensional data, yet careful selection of the kernel function and parameters is required. The Gaussian Mixture Model can fit complex data distributions, but it requires numerous parameter estimations.
Subsequently, this research conducts comparative analyses of six different anomaly detection techniques, namely One-Class SVM, Gaussian Mixture Model, Autoencoder, Isolation Forest, Local Outlier Factor, and Ensemble Voting of the former five methods. The six models are tested empirically on five different datasets, with their performance on each dataset evaluated using F1-score and Balanced Accuracy.
Ultimately, the research findings indicate that while the Isolation Forest demonstrates substantial performance on certain specific datasets, the Ensemble Voting model performs excellently across all datasets.

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