Summary of Dtor: Decision Tree Outlier Regressor to Explain Anomalies, by Riccardo Crupi et al.
DTOR: Decision Tree Outlier Regressor to explain anomalies
by Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino, Immacolata Marano, Massimiliano Brinis, Luca Albertazzi, Andrea Cirillo, Andrea Claudio Cosentini
First submitted to arxiv on: 16 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 In this paper, researchers propose a novel technique called Decision Tree Outlier Regressor (DTOR) for generating rule-based explanations of individual data points identified as outliers by machine learning models. The DTOR method leverages decision trees to estimate anomaly scores and extract relevant rules from the model’s predictions. The authors demonstrate the effectiveness of DTOR in datasets with many features, achieving robust performance comparable to Anchors while reducing execution time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand why some data points don’t fit a pattern. It’s like trying to figure out what’s wrong with a weird photo on your phone. Sometimes things go wrong because someone did something bad, or maybe there was an accident. To stop these bad things from happening again, we need to know what went wrong. Machine learning can help us find those weird points, but it’s hard to understand why they’re weird without some extra information. The Decision Tree Outlier Regressor (DTOR) is a new way to figure out why individual data points are outliers, using decision trees and rules to make sense of the patterns. |
Keywords
* Artificial intelligence * Decision tree * Machine learning
