DTOR: Decision Tree Outlier Regressor to explain anomalies
Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more wid...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
16-03-2024
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Explaining outliers occurrence and mechanism of their occurrence can be
extremely important in a variety of domains. Malfunctions, frauds, threats, in
addition to being correctly identified, oftentimes need a valid explanation in
order to effectively perform actionable counteracts. The ever more widespread
use of sophisticated Machine Learning approach to identify anomalies make such
explanations more challenging. We present the Decision Tree Outlier Regressor
(DTOR), a technique for producing rule-based explanations for individual data
points by estimating anomaly scores generated by an anomaly detection model.
This is accomplished by first applying a Decision Tree Regressor, which
computes the estimation score, and then extracting the relative path associated
with the data point score. Our results demonstrate the robustness of DTOR even
in datasets with a large number of features. Additionally, in contrast to other
rule-based approaches, the generated rules are consistently satisfied by the
points to be explained. Furthermore, our evaluation metrics indicate comparable
performance to Anchors in outlier explanation tasks, with reduced execution
time. |
|---|---|
| DOI: | 10.48550/arxiv.2403.10903 |