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A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
Author1, Author2, Author3, Author4, Author5
decision treesscalabilitymachine learningoptimization
2609.38194
Builder Relevance
1h ago80%
Abstract
This paper proposes a method to train near-optimal deep classification trees on large-scale datasets, addressing scalability and interpretability challenges.
Reality Card
Core Claim
The proposed method achieves higher testing accuracy and greater scalability for deep classification trees compared to existing heuristic baselines.
Method / Result
The method significantly boosts efficiency for deeper structures while maintaining global optimality.
Limitations
The approximation method may introduce variability in results, affecting reproducibility.
Paper to code
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