Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach

TL;DR

This paper introduces an optimal MIP-based ensemble weighting method for rare event detection in imbalanced multi-class datasets, significantly improving accuracy and metrics over existing schemes.

Contribution

It presents a novel mixed integer programming approach that optimally weights classifiers per class and selects classifiers, enhancing robustness and performance in imbalanced datasets.

Findings

MIP-based method outperforms six existing weighting schemes.

Achieves up to 7.31% improvement in balanced accuracy.

Maintains computational efficiency while improving metrics.

Abstract

To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting scheme. Our approach leverages the diverse capabilities of the classifier ensemble on a granular per class basis, while optimizing the weights of classifier-class pairs using elastic net regularization for improved robustness and generalization. Additionally, it seamlessly and optimally selects a predefined number of classifiers from a given set. We evaluate and compare our MIP-based method against six well-established weighting schemes, using representative datasets and suitable metrics, under various ensemble sizes. The experimental results reveal that MIP outperforms all existing approaches, achieving an improvement in balanced accuracy ranging from 0.99% to 7.31%, with an overall average of 4.53% across all datasets and ensemble sizes. Furthermore, it attains an overall average increase of 4.63%, 4.60%, and 4.61% in macro-averaged precision, recall, and F1-score, respectively, while maintaining computational efficiency.