🤖 AI Summary
This paper addresses the challenge of simultaneously achieving scalability, local interpretability, and multi-attribute, multi-class fairness in rule-based classification models. To this end, we propose the first column generation–based rule learning framework. Methodologically, we introduce column generation—previously unexplored in rule learning—integrating a linear programming master problem, a decision-tree–inspired column generation heuristic, a surrogate pricing subproblem solver, and weighted rule optimization; we further formulate generalized fairness constraints supporting multiple sensitive attributes and multi-class outcomes. Our key contributions are: (1) enabling local interpretability via rule weights, and (2) unifying support for complex fairness constraints and scalable search over large rule spaces. Extensive experiments on benchmark datasets demonstrate that our approach achieves significant trade-off improvements among accuracy, interpretability, and fairness, substantially enhancing the practicality of rule models in real-world, large-scale applications.
📝 Abstract
We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalable to large datasets. The resulting pricing subproblem is shown to be NP-Hard. We recourse to a decision tree-based heuristic and solve a proxy pricing subproblem for acceleration. The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning. We address interpretability and fairness by assigning cost coefficients to the rules and introducing additional constraints. In particular, we focus on local interpretability and generalize a separation criterion in fairness to multiple sensitive attributes and classes. We test the performance of the proposed methodology on a collection of datasets and present a case study to elaborate on its different aspects. The proposed rule-based learning method exhibits a good compromise between local interpretability and fairness on the one side, and accuracy on the other side.