Summary of Unsupervised Anomaly Detection For Tabular Data Using Noise Evaluation, by Wei Dai et al.
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by Wei Dai, Kai Hwang, Jicong Fan
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel unsupervised anomaly detection (UAD) method for tabular data, which learns a deep neural network from clean and noisy training datasets. The proposed approach guarantees reliable decision boundaries between normal and anomalous data without requiring real anomalous data in the training stage. Extensive experiments on 60 benchmark datasets demonstrate its effectiveness compared to 12 UAD baselines, achieving an average AUC score of 92.27% and ranking score of 1.68. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find unusual patterns in data without any examples of what’s considered “unusual”. It’s like trying to spot a stranger in a crowd by looking at how similar they are to the people you know. The method uses a special kind of neural network that can learn from both normal and noisy (or messy) data. This is important because it means we don’t need any specific examples of what’s unusual before we start finding anomalies. In tests, this method did really well compared to other similar methods, and it’s also easy to use. |
Keywords
» Artificial intelligence » Anomaly detection » Auc » Neural network » Unsupervised
