Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
CoRR(2024)
摘要
Detection of anomalous situations for complex mission-critical systems holds
paramount importance when their service continuity needs to be ensured. A major
challenge in detecting anomalies from the operational data arises due to the
imbalanced class distribution problem since the anomalies are supposed to be
rare events. This paper evaluates a diverse array of machine learning-based
anomaly detection algorithms through a comprehensive benchmark study. The paper
contributes significantly by conducting an unbiased comparison of various
anomaly detection algorithms, spanning classical machine learning including
various tree-based approaches to deep learning and outlier detection methods.
The inclusion of 104 publicly available and a few proprietary industrial
systems datasets enhances the diversity of the study, allowing for a more
realistic evaluation of algorithm performance and emphasizing the importance of
adaptability to real-world scenarios. The paper dispels the deep learning myth,
demonstrating that though powerful, deep learning is not a universal solution
in this case. We observed that recently proposed tree-based evolutionary
algorithms outperform in many scenarios. We noticed that tree-based approaches
catch a singleton anomaly in a dataset where deep learning methods fail. On the
other hand, classical SVM performs the best on datasets with more than 10
anomalies, implying that such scenarios can be best modeled as a classification
problem rather than anomaly detection. To our knowledge, such a study on a
large number of state-of-the-art algorithms using diverse data sets, with the
objective of guiding researchers and practitioners in making informed
algorithmic choices, has not been attempted earlier.
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