Publications

The algorithms in pytrees-rs were introduced in the following papers. If you use them in your work, please cite the relevant one.

Time Constrained DL8.5 Using Limited Discrepancy Search. H. Kiossou, P. Schaus, S. Nijssen and V. R. Houndji. ECML PKDD 2022, LNCS 13717, pp. 443-459. doi:10.1007/978-3-031-26419-1_27

Limited discrepancy search for DL8.5 (LDS-DL8.5), so that the search returns good trees under a time limit. In pytrees: DL85Classifier with a DiscrepancyRule.

Efficient Lookahead Decision Trees. H. Kiossou, P. Schaus, S. Nijssen and G. Aglin. IDA 2024, pp. 133-144. doi:10.1007/978-3-031-58553-1_11

LGDT, a top-down learner that chooses each test with an efficient depth-2 lookahead. In pytrees: LGDTClassifier.

A Generic Complete Anytime Beam Search for Optimal Decision Tree. H. Kiossou and P. Schaus. IDA 2026. doi:10.1007/978-3-032-23833-7_8, arXiv:2508.06064

CA-DL8.5, a framework that generalises LDS-DL8.5 and Top-k-DL8.5: rules restrict each pass of the search and are relaxed at each restart. In pytrees: the search rules of DL85Classifier.

Anytime Optimal Decision Tree Learning with Continuous Features. H. Kiossou, P. Schaus and S. Nijssen. ECML PKDD 2026. arXiv:2601.14765

An anytime version of ConTree based on limited discrepancy search. In pytrees: ConTreeClassifier with use_lds=True or fit_anytime.

Related work

• G. Aglin, S. Nijssen and P. Schaus. Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. AAAI 2020. DL8.5; the original implementation is pydl8.5.
• E. Demirović, A. Lukina, E. Hebrard, J. Chan, J. Bailey, C. Leckie, K. Ramamohanarao and P. J. Stuckey. MurTree: Optimal Decision Trees via Dynamic Programming and Search. JMLR 23, 2022. The depth-2 solver used by DL8.5 and LGDT.
• C. E. Briţa, J. G. M. van der Linden and E. Demirović. Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound. AAAI 2025. ConTree; the original implementation is ConSol-Lab/contree.