Adaptive Denoising and Evolutionary Ensemble Learning for Imbalanced Financial Data Classification

Abstract

In financial risk-control scenarios, complex data structures and high costs of misclassification impose greater demands on classification models. To enhance model discrimination under noise interference and class imbalance, an imbalanced financial data classification model is developed by integrating adaptive denoising technology and evolutionary ensemble learning. At the data level, the model performs noise-aware weighting and minority oversampling to achieve sample balance. At the model level, a multi-objective particle swarm optimization algorithm was employed to evolve and optimize the weights of multiple sub-classifiers, forming a self-adaptive ensemble structure. Experiments conducted on three public datasets, Lending Club, Credit Card Fraud Detection, and Give Me Some Credit, showed that the model achieved F1-scores of 92.1%, 90.8%, and 88.4%, respectively. The model maintained high classification accuracy and stability under different data distributions and operating conditions. Further simulation tests demonstrate consistent convergence and strong robustness, confirming the applicability and generalization capability of the model in complex financial data environments.