IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation

TL;DR

The paper introduces iPOF, a simple yet powerful outlier detection booster that employs infinite score propagation to significantly improve detection performance across various datasets.

Contribution

The paper proposes iPOF, a novel outlier detection method that uses infinite propagation of outlier scores to enhance existing score-based detectors with minimal complexity.

Findings

iPOF improves detection accuracy by 2% to 46% on average.

In some cases, iPOF boosts performance over 3000%.

The method is effective and efficient across multiple datasets.

Abstract

Outlier detection is one of the most popular and continuously rising topics in the data mining field due to its crucial academic value and extensive industrial applications. Among different settings, unsupervised outlier detection is the most challenging and practical one, which attracts tremendous efforts from diverse perspectives. In this paper, we consider the score-based outlier detection category and point out that the performance of current outlier detection algorithms might be further boosted by score propagation. Specifically, we propose Infinite Propagation of Outlier Factor (iPOF) algorithm, an extremely and excitingly simple outlier detection booster via infinite propagation. By employing score-based outlier detectors for initialization, iPOF updates each data point's outlier score by averaging the outlier factors of its nearest common neighbors. Extensive experimental results on numerous datasets in various domains demonstrate the effectiveness and efficiency of iPOF significantly over several classical and recent state-of-the-art methods. We also provide the parameter analysis on the number of neighbors, the unique parameter in iPOF, and different initial outlier detectors for general validation. It is worthy to note that iPOF brings in positive improvements ranging from 2% to 46% on the average level, and in some cases, iPOF boosts the performance over 3000% over the original outlier detection algorithm.