AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm
This work addresses anomaly detection for data analysis, offering a novel method and explanation tool, but it appears incremental as it builds on existing isolation and distance concepts.
The authors tackled anomaly detection by developing AIDA, a parameter-free algorithm that combines distance and isolation metrics without nearest-neighbors, and introduced TIX for anomaly explanation, showing competitive performance with state-of-the-art methods and superiority in finding outliers in multidimensional subspaces.
We combine the metrics of distance and isolation to develop the Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm. AIDA is the first distance-based method that does not rely on the concept of nearest-neighbours, making it a parameter-free model. Differently from the prevailing literature, in which the isolation metric is always computed via simulations, we show that AIDA admits an analytical expression for the outlier score, providing new insights into the isolation metric. Additionally, we present an anomaly explanation method based on AIDA, the Tempered Isolation-based eXplanation (TIX) algorithm, which finds the most relevant outlier features even in data sets with hundreds of dimensions. We test both algorithms on synthetic and empirical data: we show that AIDA is competitive when compared to other state-of-the-art methods, and it is superior in finding outliers hidden in multidimensional feature subspaces. Finally, we illustrate how the TIX algorithm is able to find outliers in multidimensional feature subspaces, and use these explanations to analyze common benchmarks used in anomaly detection.