Dynamic Threshold Selection in Genetic Programming for Imbalanced Fault Diagnosis

Imbalanced datasets are a major challenge in industrial fault diagnosis because the majority of data belong to the non-fault class, while fault instances constitute a small minority. Genetic Programming (GP) has shown great potential in handling imbalanced classification tasks due to its ability to evolve classifiers and automatically optimize decision rules. Traditional GP methods for imbalanced classification often rely on fixed decision thresholds (e.g., 0 or 0.5). However, such thresholds fail to adapt to varying data distributions, resulting in limited accuracy in fault diagnosis. While threshold-free methods, such as GP using the Area Under the Curve (AUC) and its variants as fitness functions, have demonstrated effectiveness, practical applications in industrial systems often require explicit thresholds to generate accurate class labels. This paper introduces a GP-based approach with a simplified AUC variant as the fitness function and a dynamic threshold search mechanism. By adaptively optimizing thresholds during evolution, the method improves minority class detection. Experiments on public fault diagnosis datasets with varying imbalance ratios demonstrate that the proposed approach consistently outperforms traditional GP methods.