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Code and Data Repository for Mixed Integer Linear Optimization Formulations for Learning Optimal Binary Classification Trees

Brandon Alston et al.

INFORMS Journal on Computing2026https://doi.org/10.1287/ijoc.2023.0068.cdarticle
AJG 3ABDC A
Weight
0.37

Abstract

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Mixed Integer Linear Optimization Formulations for Learning Optimal Binary Classification Trees by Brandon Alston, Hamidreza Validi, and Illya V. Hicks.

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https://doi.org/https://doi.org/10.1287/ijoc.2023.0068.cd

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@article{brandon2026,
  title        = {{Code and Data Repository for Mixed Integer Linear Optimization Formulations for Learning Optimal Binary Classification Trees}},
  author       = {Brandon Alston et al.},
  journal      = {INFORMS Journal on Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/ijoc.2023.0068.cd},
}

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Code and Data Repository for Mixed Integer Linear Optimization Formulations for Learning Optimal Binary Classification Trees

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.