Yet Another Representation of Binary Decision Trees: A Mathematical Demonstration
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Abstract (arXiv)
A decision tree looks like a simple directed acyclic computational graph, where only the leaf nodes specify the output values and the non-terminals specify their tests or split conditions. From the numerical perspective, we express decision trees in the language of computational graph. We explicitly parameterize the test phase, traversal phase and prediction phase of decision trees based on the bitvectors of non-terminal nodes. As shown, the decision tree is a shallow binary network in some sense. Especially, we introduce the bitvector matrix to implement the tree traversal in numerical approach, where the core is to convert the logical `AND' operation to arithmetic operations. And we apply this numerical representation to extend and unify diverse decision trees in concept.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 18 Jan 2021.
Builds on
15 of 46 references.
References · 15
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3 more
- 2019The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDEcited by 3
- 2018Representation of ordered trees with a given degree distributioncited by 1
- 2019Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Networkscited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.