SAT-based Decision Tree Learning for Large Data Sets
Keywords:Satisfiability, Classification and Regression
AbstractDecision 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 real-world 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.
How to Cite
Schidler, A., & Szeider, S. (2021). SAT-based Decision Tree Learning for Large Data Sets. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 3904-3912. https://doi.org/10.1609/aaai.v35i5.16509
AAAI Technical Track on Constraint Satisfaction and Optimization