A Multiobjective Genetic Algorithm Based Unilateral Rule Extraction Model for Credit Risk Classification

This paper proposes a two-phase unilateral rule extraction method based on a multiobjective genetic algorithm (URE-MOGA) to solve the challenges of sparse categorical features and model interpretability in credit risk classification tasks. In this method, rule encoding is employed to represent the sparse categorical features without increasing data dimensionality. The Pareto dominance theory is utilized to balance the trade-off between model accuracy and interpretability. Additionally, a diversity function and unilateral rule learning strategy are designed to facilitate the acquisition of reasonable rules and classifiers by the URE-MOGA model. Furthermore, three publicly available datasets, along with six derived datasets, are used to validate the feasibility of the proposed URE-MOGA model. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, interpretability, and robustness. The adoption of a multiobjective approach enables the URE-MOGA model to extract more concise and comprehensible classification rules while ensuring high accuracy levels. Moreover, both the unilateral rule learning strategy and diversity function play vital roles in enhancing classifier accuracy and robustness within the URE-MOGA model. Overall, the proposed URE-MOGA model provides a novel insight into categorical feature sparsity handling interpretable modeling for credit risk classification tasks.