Robust Outlier Detection Method Based on Local Entropy and Global Density

TL;DR

This paper introduces EDROD, a robust outlier detection method combining global density and local entropy to accurately identify point and cluster anomalies across diverse datasets.

Contribution

The paper proposes EDROD, a novel anomaly detection approach that effectively detects various anomalies and demonstrates robustness to parameter variations and dataset characteristics.

Findings

EDROD outperforms existing methods on synthetic and real datasets.

EDROD detects both point and cluster anomalies simultaneously.

EDROD shows strong robustness to parameter changes.

Abstract

By now, most outlier-detection algorithms struggle to accurately detect both point anomalies and cluster anomalies simultaneously. Furthermore, a few K-nearest-neighbor-based anomaly-detection methods exhibit excellent performance on many datasets, but their sensitivity to the value of K is a critical issue that needs to be addressed. To address these challenges, we propose a novel robust anomaly detection method, called Entropy Density Ratio Outlier Detection (EDROD). This method incorporates the probability density of each sample as the global feature, and the local entropy around each sample as the local feature, to obtain a comprehensive indicator of abnormality for each sample, which is called Entropy Density Ratio (EDR) for short in this paper. By comparing several competing anomaly detection methods on both synthetic and real-world datasets, it is found that the EDROD method can detect both point anomalies and cluster anomalies simultaneously with accurate performance. In addition, it is also found that the EDROD method exhibits strong robustness to the number of selected neighboring samples, the dimension of samples in the dataset, and the size of the dataset. Therefore, the proposed EDROD method can be applied to a variety of real-world datasets to detect anomalies with accurate and robust performances.