Summary of Improving Interpretability Of Scores in Anomaly Detection Based on Gaussian-bernoulli Restricted Boltzmann Machine, by Kaiji Sekimoto and Muneki Yasuda
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
by Kaiji Sekimoto, Muneki Yasuda
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 The paper proposes a new approach to semi-supervised anomaly detection using Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs). GBRBMs are trained on normal data points, and then used to classify both normal and anomalous data based on an energy function. However, the classification score lacks interpretability, making it difficult to set a suitable threshold. The proposed solution is to use the cumulative distribution of the score as an interpretable measure, which can be used to guide the threshold setting. Numerical experiments demonstrate the effectiveness of this approach. Additionally, the paper proposes an evaluation method for the minimum score value using simulated annealing, which is widely used for optimization problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve anomaly detection by making it easier to understand why certain data points are considered abnormal or normal. It does this by creating a new way to look at the scores given to each point. This makes it simpler to decide what score means something is an outlier and what score means it’s just regular data. The researchers tested their method with real-world data and showed that it works well. |
Keywords
* Artificial intelligence * Anomaly detection * Classification * Optimization * Semi supervised
