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A. Schidler, S. Szeider:
"SAT-based Decision Tree Learning for Large Data Sets";
Talk: 35th AAAI 2021, virtual event; 2021-02-02 - 2021-02-09; in: "Thirty-Fifth AAAI Conference on Artificial Intelligence", AAAI Press, 35 (2021), ISBN: 978-1-57735-866-4; 3904 - 3912.

Decision trees of low depth are beneficial for understanding
and interpreting the data they represent. Unfortunately, finding
a decision tree of lowest depth that correctly represents given
data is NP-hard. Hence known algorithms either (i) utilize
heuristics that do not optimize the depth or (ii) are exact but
scale only to small or medium-sized instances. We propose
a new hybrid approach to decision tree learning, combining
heuristic and exact methods in a novel way. More specifically,
we employ SAT encodings repeatedly to local parts of a decision
tree provided by a standard heuristic, leading to a global
depth improvement. This allows us to scale the power of exact
SAT-based methods to almost arbitrarily large data sets. We
evaluate our new approach experimentally on a range of realworld
instances that contain up to several thousand samples. In
almost all cases, our method successfully decreases the depth
of the initial decision tree; often, the decrease is significant.

https://publik.tuwien.ac.at/files/publik_300054.pdf