Fluctuation-based Outlier Detection

TL;DR

This paper introduces FBOD, a novel outlier detection method based on fluctuation analysis that is fast, effective, and does not rely on traditional distance or density measures, outperforming existing algorithms.

Contribution

The paper presents a new fluctuation-based outlier detection method that is computationally efficient and fundamentally different from existing approaches, using graph propagation and fluctuation comparison.

Findings

FBOD outperforms seven state-of-the-art algorithms on multiple datasets.

FBOD achieves only 5% of the execution time of the fastest existing method.

Experimental results demonstrate the effectiveness and efficiency of FBOD.

Abstract

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with seven state-of-the-art algorithms on eight real-world tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection.