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Documents authored by Shati, Pouya


Document
SAT-Based Learning of Compact Binary Decision Diagrams for Classification

Authors: Pouya Shati, Eldan Cohen, and Sheila McIlraith

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)


Abstract
Decision trees are a popular classification model in machine learning due to their interpretability and performance. However, the number of splits in decision trees grow exponentially with their depth which can incur a higher computational cost, increase data fragmentation, hinder interpretability, and restrict their applicability to memory-constrained hardware. In constrast, binary decision diagrams (BDD) utilize the same split across each level, leading to a linear number of splits in total. Recent work has considered optimal binary decision diagrams (BDD) as compact and accurate classification models, but has only focused on binary datasets and has not explicitly optimized the compactness of the resulting diagrams. In this work, we present a SAT-based encoding for a multi-terminal variant of BDDs (MTBDDs) that incorporates a state-of-the-art direct encoding of numerical features. We then develop and evaluate different approaches to explicitly optimize the compactness of the diagrams. In one family of approaches, we learn a tree BDD first and model the size of the diagram the tree will be reduced to as a secondary objective, in a one-stage or two-stage optimization scheme. Alternatively, we directly learn diagrams that support multi-dimensional splits for improved expressiveness. Our experiments show that direct encoding of numerical features leads to better performance. Furthermore, we show that exact optimization of size leads to more compact solutions while maintaining higher accuracy. Finally, our experiments show that multi-dimensional splits are a viable approach to achieving higher expressiveness with a lower computational cost.

Cite as

Pouya Shati, Eldan Cohen, and Sheila McIlraith. SAT-Based Learning of Compact Binary Decision Diagrams for Classification. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 33:1-33:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{shati_et_al:LIPIcs.CP.2023.33,
  author =	{Shati, Pouya and Cohen, Eldan and McIlraith, Sheila},
  title =	{{SAT-Based Learning of Compact Binary Decision Diagrams for Classification}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{33:1--33:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.33},
  URN =		{urn:nbn:de:0030-drops-190700},
  doi =		{10.4230/LIPIcs.CP.2023.33},
  annote =	{Keywords: Binary Decision Diagram, Classification, Compactness, Numeric Data, MaxSAT}
}
Document
SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features

Authors: Pouya Shati, Eldan Cohen, and Sheila McIlraith

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)


Abstract
Decision trees are a popular classification model in machine learning due to their interpretability and performance. Traditionally, decision-tree classifiers are constructed using greedy heuristic algorithms, however these algorithms do not provide guarantees on the quality of the resultant trees. Instead, a recent line of work has studied the use of exact optimization approaches for constructing optimal decision trees. Most of the recent approaches that employ exact optimization are designed for datasets with binary features. While numeric and categorical features can be transformed to binary features, this transformation can introduce a large number of binary features and may not be efficient in practice. In this work, we present a novel SAT-based encoding for decision trees that supports non-binary features and demonstrate how it can be used to solve two well-studied variants of the optimal decision tree problem. We perform an extensive empirical analysis that shows our approach obtains superior performance and is often an order of magnitude faster than the current state-of-the-art exact techniques on non-binary datasets.

Cite as

Pouya Shati, Eldan Cohen, and Sheila McIlraith. SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 50:1-50:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


Copy BibTex To Clipboard

@InProceedings{shati_et_al:LIPIcs.CP.2021.50,
  author =	{Shati, Pouya and Cohen, Eldan and McIlraith, Sheila},
  title =	{{SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{50:1--50:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.50},
  URN =		{urn:nbn:de:0030-drops-153416},
  doi =		{10.4230/LIPIcs.CP.2021.50},
  annote =	{Keywords: Decision Tree, Classification, Numeric Data, Categorical Data, SAT, MaxSAT}
}
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