DTOR: Decision Tree Outlier Regressor for Anomaly Explanation

Kernekoncepter

Explaining outliers occurrence is crucial in various domains, and the Decision Tree Outlier Regressor (DTOR) provides rule-based explanations for individual data points by estimating anomaly scores.

Resumé

The DTOR technique aims to produce transparent explanations for anomaly detection decisions. It utilizes a Decision Tree Regressor to compute estimation scores and extract the relative path associated with the data point score. Results show robustness even in datasets with numerous features. Compared to other approaches, DTOR's rules are consistently satisfied by the points being explained. Evaluation metrics indicate comparable performance to Anchors in outlier explanation tasks but with reduced execution time. Anomaly detection plays a significant role in internal audit activities within the banking sector, helping identify atypicalities and potential risks. XAI techniques like SHAP and DIFFI offer insights into model predictions and feature importance. Rule-based XAI methods like Anchors provide human-interpretable rules for model predictions, enhancing transparency and trust in AI-driven decisions.

Statistik

Execution time: 13.17 (19.20)

Precision: 0.54 ± 0.03

Coverage: 0.00 ± 0.00

Validity %: 6%

Rule length: 0.40

Execution time: 24.88 (32.24)

Precision: 0.90 ± 0.18

Coverage: 0.15 ± 0.17

Validity %: 100%

Rule length: 6.14
(Additional data available in the content)

Citater

"The ever more widespread use of sophisticated Machine Learning approach to identify anomalies make such explanations more challenging."
"Our results demonstrate the robustness of DTOR even in datasets with a large number of features."
"Anchors enhance interpretability and facilitates trust in AI-driven decisions."

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DTOR

by Riccardo Cru... kl. arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10903.pdf

Dybere Forespørgsler

How can explainable AI techniques like DTOR impact decision-making processes beyond anomaly detection

Explainable AI techniques like DTOR can have a significant impact on decision-making processes beyond anomaly detection by enhancing transparency, accountability, and trust in the models' predictions. By providing rule-based explanations for individual data points, DTOR enables stakeholders to understand the factors driving model decisions. This understanding is crucial in various domains where actionable insights are required based on AI outputs. For instance, in the banking sector discussed in the context, internal auditors can leverage these explanations to identify potential risks, detect frauds, and make informed recommendations for improvement. The interpretability offered by DTOR empowers decision-makers to effectively address issues and enhance overall system integrity and security.

What are potential drawbacks or limitations of rule-based XAI methods like Anchors compared to DTOR

While rule-based XAI methods like Anchors offer transparent and comprehensible explanations for model predictions, they come with certain drawbacks compared to DTOR. One limitation of Anchors is its inability to handle regression tasks efficiently as it is primarily designed for classification problems. On the other hand, DTOR specifically addresses anomaly detection through regression modeling, making it more suitable for explaining anomalies accurately. Additionally, Anchors may struggle with generating rules that are consistently satisfied by all data points to be explained due to their design limitations or complexity of datasets. In contrast, DTOR demonstrates robustness even in datasets with a large number of features and ensures that generated rules align well with the anomalies being detected.

How might advancements in XAI techniques influence the future of anomaly detection practices

Advancements in XAI techniques are poised to revolutionize anomaly detection practices by improving model interpretability and explainability. As XAI methods evolve to provide more accurate and understandable explanations for complex models like those used in anomaly detection algorithms (e.g., Isolation Forest), analysts will gain deeper insights into why certain instances are flagged as anomalies. This enhanced understanding can lead to better-informed decision-making processes when addressing outliers or anomalous behavior within datasets. Moreover, advancements in XAI may pave the way for developing hybrid approaches that combine different explanation techniques such as feature importance methods with rule-based approaches like DTOR—further refining anomaly detection practices towards greater efficiency and effectiveness.