MOSS: Multi-Objective Optimization for Stable Rule Sets
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3711896.3737055.pdf
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2.5 MB
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Adobe PDF
Checksum (MD5)
4cc7d65db9d5790d89494f2407e61c2b
Author(s)
Liu, Brian
Mazumder, Rahul
Date Issued
August 3, 2025
Publisher
ACM|Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Citation
Brian Liu and Rahul Mazumder. 2025. MOSS: Multi-Objective Optimization for Stable Rule Sets. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '25). Association for Computing Machinery, New York, NY, USA, 1753–1764.
Version
Final published version
Abstract
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapidly compute the Pareto frontier between these two objectives, and our algorithm scales to problem instances beyond the capabilities of commercial optimization solvers. Our experiments show that MOSS outperforms state-of-the-art rule ensembles in terms of both predictive performance and stability.
Description
KDD ’25, Toronto, ON, Canada
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3711896.3737055
