CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets

Abstract: Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where rare but critical events-such as fraudulent transactions or medical anomalies-must be identified accurately. Traditional generative models offer a potential remedy through data augmentation but often treat generation and classification as independent processes, leading to distribution mismatch and limited classifier benefit. To address these shortcomings, we propose Causal Cooperative Networks (CCNETS), a modular learning framework that integrates generation, inference, and reconstruction within a unified causal paradigm. CCNETS comprises three cooperative modules: an Explainer for latent feature abstraction, a Reasoner for label prediction, and a Producer for context-aware data generation. These components interact through a causal feedback loop, where classification results guide targeted sample synthesis. A key innovation, the Zoint mechanism, enables adaptive fusion of latent and observable features, enhancing semantic richness and enabling robust decision-making under uncertainty. We evaluate CCNETS on a real-world credit card fraud detection dataset with extreme imbalance (fraud cases < 0.2%). Across three experimental setups-including synthetic training, amplified generation, and direct classifier comparison-CCNETS outperforms baseline methods, achieving higher F1 scores, precision, and recall. Models trained on CCNETS-generated data also demonstrate superior generalization under limited data conditions. These results establish CCNETS as a scalable, interpretable, and hybrid soft computing framework. By causally aligning synthetic data with classifier objectives, CCNETS advances imbalanced pattern recognition and opens new directions for robust, modular learning in real-world applications.