A Mallows-like Criterion for Anomaly Detection with Random Forest Implementation

TL;DR

This paper introduces a novel model averaging criterion integrated into Random Forests, enhancing anomaly detection accuracy and robustness on diverse benchmark datasets, especially for imbalanced data scenarios.

Contribution

It proposes a new weighting criterion for model aggregation using focal loss, specifically tailored for anomaly detection within Random Forests, improving performance over traditional methods.

Findings

Outperforms traditional model averaging techniques.

Achieves higher accuracy in anomaly detection tasks.

Demonstrates robustness across various benchmark datasets.

Abstract

The effectiveness of anomaly signal detection can be significantly undermined by the inherent uncertainty of relying on one specified model. Under the framework of model average methods, this paper proposes a novel criterion to select the weights on aggregation of multiple models, wherein the focal loss function accounts for the classification of extremely imbalanced data. This strategy is further integrated into Random Forest algorithm by replacing the conventional voting method. We have evaluated the proposed method on benchmark datasets across various domains, including network intrusion. The findings indicate that our proposed method not only surpasses the model averaging with typical loss functions but also outstrips common anomaly detection algorithms in terms of accuracy and robustness.