Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study

• URL: http://arxiv.org/abs/2402.07281v2
• Date: Mon, 26 Feb 2024 04:56:07 GMT
• Title: Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
• Authors: Santonu Sarkar, Shanay Mehta, Nicole Fernandes, Jyotirmoy Sarkar and Snehanshu Saha
• Abstract summary: This paper evaluates a diverse array of machine learning-based anomaly detection algorithms. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms.
• Score: 0.6291443816903801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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