Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection

TL;DR

This paper introduces FuBIF, a flexible and interpretable extension of the Isolation Forest algorithm that uses real-valued functions for better anomaly detection and feature importance assessment.

Contribution

The paper presents FuBIF, a unifying framework that generalizes Isolation Forest with real-valued functions, enhancing flexibility and interpretability in anomaly detection.

Findings

FuBIF outperforms traditional Isolation Forest in complex datasets.

FuBIFFI provides meaningful feature importance scores.

Open-source implementation facilitates further research.

Abstract

Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branching, significantly enhancing the flexibility of evaluation tree construction. Complementing this, the FuBIF Feature Importance (FuBIFFI) algorithm extends the interpretability in IF-based approaches by providing feature importance scores across possible FuBIF models. This paper details the operational framework of FuBIF, evaluates its performance against established methods, and explores its theoretical contributions. An open-source implementation is provided to encourage further research and ensure reproducibility.