A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
This addresses classification problems in unbalanced data scenarios, such as credit card fraud detection, but appears incremental as it builds on existing probabilistic modeling approaches.
The paper tackles classification bias and poor minority class recognition in unbalanced datasets by proposing a Markov network model with joint probability distributions and conditional dependencies. Experimental results on a credit card fraud detection dataset show the model significantly outperforms traditional classifiers like logistic regression, SVM, random forest, and XGBoost in weighted accuracy, F1 score, and AUC-ROC.
This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class distribution. By constructing joint probability distribution and conditional dependency, the model can achieve global modeling and reasoning optimization of sample categories. The study introduced marginal probability estimation and weighted loss optimization strategies, combined with regularization constraints and structured reasoning methods, effectively improving the generalization ability and robustness of the model. In the experimental stage, a real credit card fraud detection dataset was selected and compared with models such as logistic regression, support vector machine, random forest and XGBoost. The experimental results show that the Markov network performs well in indicators such as weighted accuracy, F1 score, and AUC-ROC, significantly outperforming traditional classification models, demonstrating its strong decision-making ability and applicability in unbalanced data scenarios. Future research can focus on efficient model training, structural optimization, and deep learning integration in large-scale unbalanced data environments and promote its wide application in practical applications such as financial risk control, medical diagnosis, and intelligent monitoring.