Decision Tree Learning

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7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Ternary Decision Trees with Locally-Adaptive Uncertainty Zones
May 21, 2026William SmitsUncertainty Quantification Selective Prediction
Avathon, Austin, TX, USA
TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices
May 15, 2026Hao Wang, Kuang Zhang, Yonggang Chi +3Efficient Inference Internet of Things
Decision Tree Learning on Product Spaces
May 13, 2026Arshia Soltani Moakhar, Faraz Ghahremani, Kiarash Banihashem +1Decision Tree Learning Approximation Algorithms
Department of Computer Science, University of Maryland, College Park, USA.
Minimax Rates and Spectral Distillation for Tree Ensembles
May 12, 2026Binh Duc Vu, David S. WatsonModel Compression Gradient-Boosted Decision Trees
King’s College London
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
May 12, 2026Phu-Hoa Pham, Chi-Nguyen Tran, Nguyen Lam Phu Quy +3Class-Incremental Learning Continual Learning
Faculty of Information and Technology University of Science Vietnam National University Ho Chi Minh City, Vietnam · Department of Computer Science University of Warwick Coventry, United Kingdom
Optimal Recourse Summaries via Bi-Objective Decision Tree Learning
May 8, 2026Ioannis Chatzis, Jason Liartis, Athanasios Voulodimos +1Algorithmic Auditing Decision Tree Learning
Artificial Intelligence and Learning Systems Laboratory
FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
May 5, 2026Elitsa Yotkova, Violeta Kastreva, Dimitar Dimitrov +2AI-Generated Text Detection Decision Tree Learning
Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Bulgaria · Mohamed bin Zayed University of Artificial Intelligence, UAE
Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
May 1, 2026Sai Niranjan Ramachandran, Suvrit SraDecision Tree Learning Diffusion Models
School of Computation, Information and Technology, Technical University of Munich, Germany · Munich center for machine learning (MCML)
Interpretable Quantile Regression by Optimal Decision Trees
Apr 22, 2026Valentin Lemaire, Gaël Aglin, Siegfried NijssenQuantile Regression Interpretable ML
Euranova, Rue Emile Francqui, Mont-Saint-Guibert, Belgium · Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
Apr 21, 2026Jake LeeDecision Tree Learning Random Forest
Independent Researcher · Austin, TX, USA
TrEEStealer: Stealing Decision Trees via Enclave Side Channels
Apr 20, 2026Jonas Sander, Anja Rabich, Nick Mahling +5Model Extraction Attacks Decision Tree Learning
University of Luebeck, Luebeck, Germany · Durham University, Durham, United Kingdom
ContraPrompt: Contrastive Prompt Optimization via Dyadic Reasoning Trace Analysis
Apr 20, 2026Rishav Rishav, Pushpak Pujari, Pushpendre RastogiPairwise Comparison CoT Reasoning
Vizops AI
Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises
Aug 30, 2025Lu Han, Xiuying WangFinancial Services Interpretable ML
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China · School of Computer Science, University of Sydney, NSW 2006, Australia
Experiments with Optimal Model Trees
Mar 17, 2025Sabino Francesco Roselli, Eibe FrankInterpretable ML Decision Tree Learning
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. · Department of Computer Science, University of Waikato, Hamilton, New Zealand.

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Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

William Smits

Avathon, Austin, TX, USA

Uncertainty Quantification Selective Prediction Decision Tree Learning Classification

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Decision trees assign identical confidence to instances near and far from each split threshold. We introduce ternary decision trees, which augment each split node with an uncertainty zone of half-width delta. A decision-theoretic framework characterises the optimal zone width delta* as the solution to a node-local cost-minimisation problem; four formal properties are established: accuracy decomposition, a sufficiency condition for decided accuracy improvement, an exact efficiency characterisation (eta = Dec-Acc minus Acc_u, the accuracy gap between decided and boundary-uncertain predictions), and asymptotic consistency of the margin method. Instances within the zone receive predictions by weighted blending of both child subtrees and are flagged as boundary-uncertain. We propose and evaluate five delta-estimation methods: quality-plateau (plateau width of the split criterion curve), class-overlap (empirical class-distribution overlap), gain-ratio (split quality relative to split entropy), node-bootstrap (threshold variance under node-level resampling), and margin (SVM-inspired distance to the nearest cross-class training example). All methods reuse statistics already computed during standard CART split finding, requiring no external noise specification. Evaluated across 71 of the 72 OpenML-CC18 datasets with 5-fold cross-validation, all five methods with probabilistic routing significantly outperform standard CART on decided accuracy (Wilcoxon signed-rank, p < 0.001). The margin method achieves the best efficiency (0.104 accuracy gain per unit flagging rate), wins on 42 of 72 datasets, and requires zero hyperparameters. Analysis on Breiman synthetic benchmarks confirms margin is self-calibrating on clean data. On mammography, node-bootstrap achieves +0.71% decided accuracy by flagging 10.8% of cases as boundary-uncertain.

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