ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2022
Fast Sparse Decision Tree Optimization via Reference Ensembles.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Cell graph analysis in hepatocellular carcinoma: predicting local recurrence and identifying spatial relationship biomarkers.NPJ precision oncology · 2025Article
- Event-driven architecture and intelligent decision tree facilitated sustainable trade activity monitoring model design.PloS one · 2025Article
- Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation.Genomics, proteomics & bioinformatics · 2024Article
- Optimal Sparse Survival Trees.Proceedings of machine learning research · 2024Article
- [Recent research on machine learning in the diagnosis and treatment of necrotizing enterocolitis in neonates].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023Article
- Optimal Sparse Regression Trees.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2023Article
- Decision trees: from efficient prediction to responsible AI.Frontiers in artificial intelligence · 2023Review
- Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.CEUR workshop proceedings · 2022Article
- Exploring the Whole Rashomon Set of Sparse Decision Trees.Advances in neural information processing systems · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.