Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection

TL;DR

This paper introduces a locally adaptive one-class classifier fusion method with dynamic $ ext{l}_p$-norm constraints, improving anomaly detection accuracy and computational efficiency through interior-point optimization, validated on benchmark datasets.

Contribution

It proposes a novel fusion framework that adaptively adjusts to local data characteristics and employs an efficient interior-point method, advancing anomaly detection techniques.

Findings

Achieves up to 19-fold speed improvements over traditional methods.

Demonstrates superior performance on UCI and temporal datasets.

Statistically validated with significant advantages over existing approaches.

Abstract

This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency compared to traditional Frank-Wolfe approaches, achieving up to 19-fold speed improvements in complex scenarios. The framework is extensively evaluated on standard UCI benchmark datasets and specialized temporal sequence datasets, demonstrating superior performance across diverse anomaly types. Statistical validation through Skillings-Mack tests confirms our method's significant advantages over existing approaches, with consistent top rankings in both pure and non-pure learning scenarios. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications where rapid and accurate anomaly detection is crucial.