Summary of Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? a Benchmarking Study, by Santonu Sarkar et al.
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
by Santonu Sarkar, Shanay Mehta, Nicole Fernandes, Jyotirmoy Sarkar, Snehanshu Saha
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive evaluation of various machine learning-based anomaly detection algorithms for complex mission-critical systems. The authors address the imbalanced class distribution problem by using a diverse array of datasets, including 104 publicly available and proprietary industrial systems. The study compares classical machine learning approaches, tree-based methods, deep learning, and outlier detection techniques to determine their effectiveness in detecting anomalies. Notably, the paper debunks the myth that deep learning is always the best solution, instead highlighting the strengths of tree-based evolutionary algorithms and traditional SVM in specific scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can be taught to find unusual events in important systems like power grids or transportation networks. The problem is that these events are rare, so it’s hard for computers to learn what they look like. To solve this, the authors tested many different computer programs on a big collection of data from real-world systems. They found that some programs worked better than others depending on the type of system and the kind of unusual event. For example, some programs were really good at finding one-off events that happen once in a while, but they struggled with more frequent unusual events. This study will help people choose the right computer program to find unusual events in their own systems. |
Keywords
* Artificial intelligence * Anomaly detection * Deep learning * Machine learning * Outlier detection
