Explaining Outliers using Isolation Forest and Shapley Interactions

Visser, Roel; Fumagalli, Fabian ; Muschalik, Maximilian ; Hüllermeier, Eyke und Hammer, Barbara (23. April 2025): Explaining Outliers using Isolation Forest and Shapley Interactions. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 23. - 25. Apr 2025. In: ESANN 2025 proceesdings, pp. 117-122

Abstract

In unsupervised machine learning, Isolation Forest (IsoForest) is a widely used algorithm for the efficient detection of outliers. Identifying the features responsible for observed anomalies is crucial for practitioners, yet the ensemble nature of IsoForest complicates interpretation and comparison. As a remedy, SHAP is a prevalent method to interpret outlier scoring models by assigning contributions to individual features based on the Shapley Value (SV). However, complex anomalies typically involve interaction of features, and it is paramount for practitioners to distinguish such complex anomalies from simple cases. In this work, we propose Shapley Interactions (SIs) to enrich explanations of outliers with feature interactions. SIs, as an extension of the SV, decompose the outlier score into contributions of individual features and interactions of features up to a specified explanation order. We modify IsoForest to compute SI using TreeSHAP-IQ, an extension of TreeSHAP for tree-based models, using the shapiq package. Using a qualitative and quantitative analysis on synthetic and real-world datasets, we demonstrate the benefit of SI and feature interactions for outlier explanations over feature contributions alone.

• Item Type: Conference or Workshop Item (Poster)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824