Arithmetical Binary Decision Tree Traversals
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Abstract (arXiv)
This paper introduces a series of methods for traversing binary decision trees using arithmetic operations. We present a suite of binary tree traversal algorithms that leverage novel representation matrices to flatten the full binary tree structure and embed the aggregated internal node Boolean tests into a single binary vector. Our approach, grounded in maximum inner product search, offers new insights into decision tree.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 11 Sep 2022.
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4 of 18 references.
References · 4
- 2016A Survey on Learning to Hashcited by 119
- 2017Beyond Sparsity: Tree Regularization of Deep Models for Interpretabilitycited by 68
- 2019Optimizing for Interpretability in Deep Neural Networks with Tree Regularizationcited by 13
- 2022Transition Matrix Representation of Trees with Transposed Convolutionscited by 1
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