Preference Isolation Forest for Structure-based Anomaly Detection

• URL: http://arxiv.org/abs/2505.10876v1
• Date: Fri, 16 May 2025 05:32:25 GMT
• Title: Preference Isolation Forest for Structure-based Anomaly Detection
• Authors: Filippo Leveni, Luca Magri, Cesare Alippi, Giacomo Boracchi,
• Abstract summary: We conceive a general anomaly detection framework called Preference Isolation Forest (PIF)<n>PIF combines the benefits of adaptive isolation-based methods with the flexibility of preference embedding.<n>We propose three isolation approaches to identify anomalies: Voronoi-iForest, the most general solution, RuzHash-iForest, and Sliding-PIF.
• Score: 22.383337771018958
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general anomaly detection framework called Preference Isolation Forest (PIF), that combines the benefits of adaptive isolation-based methods with the flexibility of preference embedding. The key intuition is to embed the data into a high-dimensional preference space by fitting low-dimensional manifolds, and to identify anomalies as isolated points. We propose three isolation approaches to identify anomalies: $i$) Voronoi-iForest, the most general solution, $ii$) RuzHash-iForest, that avoids explicit computation of distances via Local Sensitive Hashing, and $iii$) Sliding-PIF, that leverages a locality prior to improve efficiency and effectiveness.

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