Rule Generation for Classification: Scalability, Interpretability, and Fairness

M. Hakan Akyüz Erasmus University Rotterdam

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 separation criterion in fairness to multiple sensitive attributes and classes. We test the performance of the proposed methodology on a collection of datasets. The proposed rule-based learning method exhibits a good compromise between local interpretability and fairness on one hand, and accuracy on the other.

Short Bio

M. Hakan Akyüz received his PhD in industrial engineering from Boğaziçi University. He worked as a postdoctoral fellow at Hong Kong University of Science and Technology until 2013. He joined the Department of Industrial Engineering at Galatasaray University in 2014 as an assistant professor and became an associate professor in 2018. He has been part of the econometrics department at Erasmus University Rotterdam since 2020. His research interests span logistics, algorithms, and machine learning. Lately, he focuses on combining machine learning and operations research techniques to address problems that arise in both fields.


Friday, January 19, 2024, 4.00 pm

IE Building, Halim Doğrusöz Auditorium (Ground Floor -03)


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