Feature Explosion: a generic optimization strategy for outlier detection algorithms

TL;DR

This paper introduces a generic optimization strategy called OSD, inspired by physics, that enhances the performance of various outlier detection algorithms without needing algorithm-specific tuning.

Contribution

The paper proposes a universal optimization plugin for outlier detection algorithms, reducing redundancy and simplifying performance improvements across different methods.

Findings

All tested algorithms improved performance on most datasets.

Average accuracy increased by 15% (AUC) and 63.7% (AP).

Demonstrates the effectiveness of a physics-inspired generic optimization strategy.

Abstract

Outlier detection tasks aim at discovering potential issues or opportunities and are widely used in cybersecurity, financial security, industrial inspection, etc. To date, thousands of outlier detection algorithms have been proposed. Clearly, in real-world scenarios, such a large number of algorithms is unnecessary. In other words, a large number of outlier detection algorithms are redundant. We believe the root cause of this redundancy lies in the current highly customized (i.e., non-generic) optimization strategies. Specifically, when researchers seek to improve the performance of existing outlier detection algorithms, they have to design separate optimized versions tailored to the principles of each algorithm, leading to an ever-growing number of outlier detection algorithms. To address this issue, in this paper, we introduce the explosion from physics into the outlier detection task and propose a generic optimization strategy based on feature explosion, called OSD (Optimization Strategy for outlier Detection algorithms). In the future, when improving the performance of existing outlier detection algorithms, it will be sufficient to invoke the OSD plugin without the need to design customized optimized versions for them. We compared the performances of 14 outlier detection algorithms on 24 datasets before and after invoking the OSD plugin. The experimental results show that the performances of all outlier detection algorithms are improved on almost all datasets. In terms of average accuracy, OSD make these outlier detection algorithms improve by 15% (AUC), 63.7% (AP).