Abstract:Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both transparency and intuitive explainability. This paper introduces the Optimal Pattern Detection Tree (OPDT), a rule-based machine learning model based on novel mixed-integer programming to discover a single optimal pattern in data through binary classification. To incorporate prior knowledge and compliance requirements, we further introduce the Branching Structure Constraints (BSC) framework, which enables decision makers to encode domain knowledge and constraints directly into the model. This optimization-based approach discovers a hidden underlying pattern in datasets, when it exists, by identifying an optimal rule that maximizes coverage while minimizing the false positive rate due to misclassification. Our computational experiments show that OPDT discovers a pattern with optimality guarantees on moderately sized datasets within reasonable runtime.
| Comments: | Published in Transactions on Machine Learning Research (TMLR). 26 pages, 4 figures. OpenReview URL: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) |
| Cite as: | arXiv:2605.14374 [cs.LG] |
| (or arXiv:2605.14374v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14374 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Transactions on Machine Learning Research (2026) |
Submission history
From: Young-Chae Hong [view email]
[v1]
Thu, 14 May 2026 04:53:18 UTC (362 KB)