Constraint-Adaptive Rule Mining in Large Databases

Decision rules are widely used due to their interpretability, efficiency, and stability in various applications, especially for financial tasks, such as fraud detection and loan assessment. In many scenarios, it is highly demanded to generate decision rules under some specific constraints. However, the performance, efficiency, and adaptivity of previous methods, which take no consideration of these constraints, is far from satisfactory in these scenarios, especially when the constraints are relatively tight. In this paper, to deal with this problem, we propose a constraint-adaptive rule mining algorithm named CARM (Constraint Adaptive Rule Mining), which is a novel decision tree based model. To provide a practical balance between purity and constraint fitness when building the trees, an adaptive criterion is designed and applied to better meet the constraints. Besides, a rule extraction and pruning process is applied to satisfy the constraints and further alleviate the overfitting problem. In addition, to improve the coverage, an iterative covering framework is proposed in this paper. Experiments on both public and business data sets show that the proposed method is able to achieve better performance, competitive efficiency, as well as low rule complexity when comparing with other methods.