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All Projects → benedekrozemberczki → Awesome Decision Tree Papers

benedekrozemberczki / Awesome Decision Tree Papers

Licence: cc0-1.0
A collection of research papers on decision, classification and regression trees with implementations.

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machine-learningxgboostrandom-forestclassifiercartlightgbmensemble-learninggradient-boostingstatistical-learninggradient-boosting-machinedecision-treetree-ensembledecision-tree-classifierclassification-treesdecision-tree-learningdecision-tree-modelclassification-modelcatboostregression-treemachine-learning-research

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Awesome Decision Tree Research Papers

Awesome PRs Welcome repo size License benedekrozemberczki⠀

A curated list of classification and regression tree research papers with implementations from the following conferences:

Similar collections about graph classification, gradient boosting, fraud detection, Monte Carlo tree search, and community detection papers with implementations.

2021

  • Online Probabilistic Label Trees (AISTATS 2021)

    • Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczyński
    • [Paper]
    • [Code]

  • Optimal Decision Trees for Nonlinear Metrics (AAAI 2021)

    • Emir Demirovic, Peter J. Stuckey
    • [Paper]

  • SAT-based Decision Tree Learning for Large Data Sets (AAAI 2021)

    • André Schidler, Stefan Szeider
    • [Paper]

  • Parameterized Complexity of Small Decision Tree Learning (AAAI 2021)

    • Sebastian Ordyniak, Stefan Szeider
    • [Paper]

  • Counterfactual Explanations for Oblique Decision Trees: Exact - Efficient Algorithms (AAAI 2021)

    • Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
    • [Paper]

  • Geometric Heuristics for Transfer Learning in Decision Trees (CIKM 2021)

    • Siddhesh Chaubal, Mateusz Rzepecki, Patrick K. Nicholson, Guangyuan Piao, Alessandra Sala
    • [Paper]

  • Fairness-Aware Training of Decision Trees by Abstract Interpretation (CIKM 2021)

    • Francesco Ranzato, Caterina Urban, Marco Zanella
    • [Paper]

  • Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification (CIKM 2021)

    • Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon
    • [Paper]

  • Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)

    • Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
    • [Paper]

  • NBDT: Neural-Backed Decision Tree (ICLR 2021)

    • Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez
    • [Paper]

  • Versatile Verification of Tree Ensembles (ICML 2021)

    • Laurens Devos, Wannes Meert, Jesse Davis
    • [Paper]

  • Connecting Interpretability and Robustness in Decision Trees through Separation (ICML 2021)

    • Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
    • [Paper]

  • Optimal Counterfactual Explanations in Tree Ensembles (ICML 2021)

    • Axel Parmentier, Thibaut Vidal
    • [Paper]

  • Efficient Training of Robust Decision Trees Against Adversarial Examples (ICML 2021)

    • Daniël Vos, Sicco Verwer
    • [Paper]

  • Learning Binary Decision Trees by Argmin Differentiation (ICML 2021)

    • Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
    • [Paper]

  • BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment (KDD 2021)

    • Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua T. Vogelstein, Randal Burns
    • [Paper]

  • ControlBurn: Feature Selection by Sparse Forests (KDD 2021)

    • Brian Liu, Miaolan Xie, Madeleine Udell
    • [Paper]

  • Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)

    • Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
    • [Paper]

  • Verifying Tree Ensembles by Reasoning about Potential Instances (SDM 2021)

    • Laurens Devos, Wannes Meert, Jesse Davis
    • [Paper]

2020

  • DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (ACL 2020)

    • Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir
    • [Paper]

  • Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)

    • Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
    • [Paper]

  • Practical Federated Gradient Boosting Decision Trees (AAAI 2020)

    • Qinbin Li, Zeyi Wen, Bingsheng He
    • [Paper]

  • Efficient Inference of Optimal Decision Trees (AAAI 2020)

  • Learning Optimal Decision Trees Using Caching Branch-and-Bound Search (AAAI 2020)

  • Abstract Interpretation of Decision Tree Ensemble Classifiers (AAAI 2020)

  • Scalable Feature Selection for (Multitask) Gradient Boosted Trees (AISTATS 2020)

    • Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian
    • [Paper]

  • Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning (AISTATS 2020)

    • Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son
    • [Paper]

  • Exploiting Categorical Structure Using Tree-Based Methods (AISTATS 2020)

  • LdSM: Logarithm-depth Streaming Multi-label Decision Trees (AISTATS 2020)

    • Maryam Majzoubi, Anna Choromanska
    • [Paper]

  • Oblique Decision Trees from Derivatives of ReLU Networks (ICLR 2020)

  • Provable Guarantees for Decision Tree Induction: the Agnostic Setting (ICML 2020)

    • Guy Blanc, Jane Lange, Li-Yang Tan
    • [Paper]

  • Decision Trees for Decision-Making under the Predict-then-Optimize Framework (ICML 2020)

    • Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
    • [Paper]

  • The Tree Ensemble Layer: Differentiability meets Conditional Computation (ICML 2020)

    • Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder
    • [Paper]
    • [Code]

  • Generalized and Scalable Optimal Sparse Decision Trees (ICML 2020)

    • Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer
    • [Paper]
    • [Code]

  • Born-Again Tree Ensembles (ICML 2020)

  • On Lp-norm Robustness of Ensemble Decision Stumps and Trees (ICML 2020)

    • Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh
    • [Paper]

  • Smaller, More Accurate Regression Forests Using Tree Alternating Optimization (ICML 2020)

    • Arman Zharmagambetov, Miguel Á. Carreira-Perpinan
    • [Paper]

  • Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)

    • Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet
    • [Paper]

  • Speeding up Very Fast Decision Tree with Low Computational Cost (IJCAI 2020)

    • Jian Sun, Hongyu Jia, Bo Hu, Xiao Huang, Hao Zhang, Hai Wan, Xibin Zhao
    • [Paper]

  • PyDL8.5: a Library for Learning Optimal Decision Trees (IJCAI 2020)

  • Geodesic Forests (KDD 2020)

    • Meghana Madhyastha, Gongkai Li, Veronika Strnadova-Neeley, James Browne, Joshua T. Vogelstein, Randal Burns
    • [Paper]

  • A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (NeurIPS 2020)

    • Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam
    • [Paper]

  • Estimating Decision Tree Learnability with Polylogarithmic Sample Complexity (NeurIPS 2020)

    • Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
    • [Paper]

  • Universal Guarantees for Decision Tree Induction via a Higher-Order Splitting Criterion (NeurIPS 2020)

    • Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
    • [Paper]

  • Smooth And Consistent Probabilistic Regression Trees (NeurIPS 2020)

    • Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim
    • [Paper]

  • An Efficient Adversarial Attack for Tree Ensembles (NeurIPS 2020)

  • Decision Trees as Partitioning Machines to Characterize their Generalization Properties (NeurIPS 2020)

    • Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
    • [Paper]

  • Evidence Weighted Tree Ensembles for Text Classification (SIGIR 2020)

    • Md. Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang
    • [Paper]

2019

  • Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System (AAAI 2019)

    • Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang
    • [Paper]

  • Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)

    • Farhad Shakerin, Gopal Gupta
    • [Paper]

  • Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (AAAI 2019)

    • Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos
    • [Paper]

  • Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals (AAAI 2019)

    • Kacper Sokol, Peter A. Flach
    • [Paper]

  • Weighted Oblique Decision Trees (AAAI 2019)

    • Bin-Bin Yang, Song-Qing Shen, Wei Gao
    • [Paper]

  • Learning Optimal Classification Trees Using a Binary Linear Program Formulation (AAAI 2019)

    • Sicco Verwer, Yingqian Zhang
    • [Paper]

  • Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees (AAAI 2019)

    • Pooya Tavallali, Peyman Tavallali, Mukesh Singhal
    • [Paper]

  • XBART: Accelerated Bayesian Additive Regression Trees (AISTATS 2019)

    • Jingyu He, Saar Yalov, P. Richard Hahn
    • [Paper]

  • Interaction Detection with Bayesian Decision Tree Ensembles (AISTATS 2019)

    • Junliang Du, Antonio R. Linero
    • [Paper]

  • Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)

    • Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei
    • [Paper]

  • Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)

  • Interpreting CNNs via Decision Trees (CVPR 2019)

    • Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu
    • [Paper]

  • EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees (ICDM 2019)

  • Fair Adversarial Gradient Tree Boosting (ICDM 2019)

    • Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki
    • [Paper]

  • Functional Transparency for Structured Data: a Game-Theoretic Approach (ICML 2019)

    • Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
    • [Paper]

  • Incorporating Grouping Information into Bayesian Decision Tree Ensembles (ICML 2019)

    • Junliang Du, Antonio R. Linero
    • [Paper]

  • Adaptive Neural Trees (ICML 2019)

    • Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori
    • [Paper]
    • [Code]

  • Robust Decision Trees Against Adversarial Examples (ICML 2019)

    • Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh
    • [Paper]
    • [Code]

  • Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples (IJCAI 2019)

    • Ariyam Das, Jin Wang, Sahil M. Gandhi, Jae Lee, Wei Wang, Carlo Zaniolo
    • [Paper]

  • FAHT: An Adaptive Fairness-aware Decision Tree Classifier (IJCAI 2019)

  • Inter-node Hellinger Distance based Decision Tree (IJCAI 2019)

  • Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)

  • A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees (IJCAI 2019)

    • Klaus Broelemann, Gjergji Kasneci
    • [Paper]

  • Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (KDD 2019)

    • Pan Li, Zhen Qin, Xuanhui Wang, Donald Metzler
    • [Paper]

  • Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers (NeurIPS 2019)

    • Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola
    • [Paper]
    • [Code]

  • Partitioning Structure Learning for Segmented Linear Regression Trees (NeurIPS 2019)

    • Xiangyu Zheng, Song Xi Chen
    • [Paper]

  • Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)

  • Optimal Decision Tree with Noisy Outcomes (NeurIPS 2019)

    • Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi
    • [Paper]
    • [Code]

  • Regularized Gradient Boosting (NeurIPS 2019)

    • Corinna Cortes, Mehryar Mohri, Dmitry Storcheus
    • [Paper]

  • Optimal Sparse Decision Trees (NeurIPS 2019)

  • MonoForest framework for tree ensemble analysis (NeurIPS 2019)

  • Calibrating Probability Estimation Trees using Venn-Abers Predictors (SDM 2019)

    • Ulf Johansson, Tuwe Löfström, Henrik Boström
    • [Paper]

  • Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization (SDM 2019)

    • Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander
    • [Paper]

  • Forest Packing: Fast Parallel, Decision Forests (SDM 2019)

    • James Browne, Disa Mhembere, Tyler M. Tomita, Joshua T. Vogelstein, Randal Burns
    • [Paper]

  • Block-distributed Gradient Boosted Trees (SIGIR 2019)

    • Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
    • [Paper]

  • Entity Personalized Talent Search Models with Tree Interaction Features (WWW 2019)

    • Cagri Ozcaglar, Sahin Cem Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan
    • [Paper]

2018

  • Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees (AAAI 2018)

    • Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé
    • [Paper]

  • MERCS: Multi-Directional Ensembles of Regression and Classification Trees (AAAI 2018)

    • Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel
    • [Paper]
    • [Code]

  • Differential Performance Debugging With Discriminant Regression Trees (AAAI 2018)

    • Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi
    • [Paper]
    • [Code]

  • Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction (AAAI 2018)

    • Jessa Bekker, Jesse Davis
    • [Paper]

  • MDP-Based Cost Sensitive Classification Using Decision Trees (AAAI 2018)

  • Generative Adversarial Image Synthesis With Decision Tree Latent Controller (CVPR 2018)

  • Enhancing Very Fast Decision Trees with Local Split-Time Predictions (ICDM 2018)

  • Realization of Random Forest for Real-Time Evaluation through Tree Framing (ICDM 2018)

    • Sebastian Buschjäger, Kuan-Hsun Chen, Jian-Jia Chen, Katharina Morik
    • [Paper]

  • Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)

    • Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke
    • [Paper]
    • [Code]

  • Learning Optimal Decision Trees with SAT (IJCAI 2018)

    • Nina Narodytska, Alexey Ignatiev, Filipe Pereira, João Marques-Silva
    • [Paper]

  • Extremely Fast Decision Tree (KDD 2018)

    • Chaitanya Manapragada, Geoffrey I. Webb, Mahsa Salehi
    • [Paper]
    • [Code]

  • RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization (KDD 2018)

    • Ting Ye, Hucheng Zhou, Will Y. Zou, Bin Gao, Ruofei Zhang
    • [Paper]

  • CatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)

    • Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
    • [Paper]
    • [Code]

  • Active Learning for Non-Parametric Regression Using Purely Random Trees (NIPS 2018)

    • Jack Goetz, Ambuj Tewari, Paul Zimmerman
    • [Paper]

  • Alternating Optimization of Decision Trees with Application to Learning Sparse Oblique Trees (NIPS 2018)

    • Miguel Á. Carreira-Perpiñán, Pooya Tavallali
    • [Paper]

  • Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)

  • Transparent Tree Ensembles (SIGIR 2018)

    • Alexander Moore, Vanessa Murdock, Yaxiong Cai, Kristine Jones
    • [Paper]

  • Privacy-aware Ranking with Tree Ensembles on the Cloud (SIGIR 2018)

    • Shiyu Ji, Jinjin Shao, Daniel Agun, Tao Yang
    • [Paper]

2017

  • Strategic Sequences of Arguments for Persuasion Using Decision Trees (AAAI 2017)

    • Emmanuel Hadoux, Anthony Hunter
    • [Paper]

  • BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)

    • Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales
    • [Paper]

  • Latency Reduction via Decision Tree Based Query Construction (CIKM 2017)

    • Aman Grover, Dhruv Arya, Ganesh Venkataraman
    • [Paper]

  • Enumerating Distinct Decision Trees (ICML 2017)

  • Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)

    • Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh
    • [Paper]
    • [Code]

  • Consistent Feature Attribution for Tree Ensembles (ICML 2017)

  • Extremely Fast Decision Tree Mining for Evolving Data Streams (KDD 2017)

    • Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer
    • [Paper]

  • CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)

    • Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
    • [Paper]
    • [Code]

  • LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)

    • Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu
    • [Paper]
    • [Code]

  • Variable Importance Using Decision Trees (NIPS 2017)

    • Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
    • [Paper]

  • A Unified Approach to Interpreting Model Predictions (NIPS 2017)

  • Pruning Decision Trees via Max-Heap Projection (SDM 2017)

    • Zhi Nie, Binbin Lin, Shuai Huang, Naren Ramakrishnan, Wei Fan, Jieping Ye
    • [Paper]

  • A Practical Method for Solving Contextual Bandit Problems Using Decision Trees (UAI 2017)

    • Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik
    • [Paper]

  • Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility (UAI 2017)

    • Hugo Gilbert, Olivier Spanjaard
    • [Paper]

  • GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)

    • Qian Zhao, Yue Shi, Liangjie Hong
    • [Paper]

2016

  • Sparse Perceptron Decision Tree for Millions of Dimensions (AAAI 2016)

    • Weiwei Liu, Ivor W. Tsang
    • [Paper]

  • Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees (CVPR 2016)

    • Jianhui Chen, Hoang Minh Le, Peter Carr, Yisong Yue, James J. Little
    • [Paper]

  • Online Learning with Bayesian Classification Trees (CVPR 2016)

    • Samuel Rota Bulò, Peter Kontschieder
    • [Paper]

  • Accurate Robust and Efficient Error Estimation for Decision Trees (ICML 2016)

  • Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)

    • Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
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  • Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)

    • Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov
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  • XGBoost: A Scalable Tree Boosting System (KDD 2016)

  • Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale (NIPS 2016)

    • Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar
    • [Paper]

  • A Communication-Efficient Parallel Algorithm for Decision Tree (NIPS 2016)

    • Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu
    • [Paper]
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  • Exploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles (SIGIR 2016)

    • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
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  • Post-Learning Optimization of Tree Ensembles for Efficient Ranking (SIGIR 2016)

    • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, Salvatore Trani
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2015

  • Particle Gibbs for Bayesian Additive Regression Trees (AISTATS 2015)

    • Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
    • [Paper]

  • DART: Dropouts Meet Multiple Additive Regression Trees (AISTATS 2015)

  • Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-level Appearance Models (CVPR 2015)

    • Jingjing Xiao, Rustam Stolkin, Ales Leonardis
    • [Paper]

  • Face Alignment Using Cascade Gaussian Process Regression Trees (CVPR 2015)

  • Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)

    • Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han
    • [Paper]

  • Entropy Evaluation Based on Confidence Intervals of Frequency Estimates : Application to the Learning of Decision Trees (ICML 2015)

    • Mathieu Serrurier, Henri Prade
    • [Paper]

  • Large-scale Distributed Dependent Nonparametric Trees (ICML 2015)

    • Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing
    • [Paper]

  • Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)

    • Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen
    • [Paper]

  • A Decision Tree Framework for Spatiotemporal Sequence Prediction (KDD 2015)

    • Taehwan Kim, Yisong Yue, Sarah L. Taylor, Iain A. Matthews
    • [Paper]

  • Efficient Non-greedy Optimization of Decision Trees (NIPS 2015)

    • Mohammad Norouzi, Maxwell D. Collins, Matthew Johnson, David J. Fleet, Pushmeet Kohli
    • [Paper]

  • QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees (SIGIR 2015)

    • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
    • [Paper]
    • [Code]

2014

  • A Mixtures-of-Trees Framework for Multi-Label Classification (CIKM 2014)

    • Charmgil Hong, Iyad Batal, Milos Hauskrecht
    • [Paper]

  • On Building Decision Trees from Large-scale Data in Applications of On-line Advertising (CIKM 2014)

    • Shivaram Kalyanakrishnan, Deepthi Singh, Ravi Kant
    • [Paper]

  • Fast Supervised Hashing with Decision Trees for High-Dimensional Data (CVPR 2014)

    • Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter
    • [Paper]

  • One Millisecond Face Alignment with an Ensemble of Regression Trees (CVPR 2014)

    • Vahid Kazemi, Josephine Sullivan
    • [Paper]

  • The return of AdaBoost.MH: multi-class Hamming trees (ICLR 2014)

  • Diagnosis Determination: Decision Trees Optimizing Simultaneously Worst and Expected Testing Cost (ICML 2014)

    • Ferdinando Cicalese, Eduardo Sany Laber, Aline Medeiros Saettler
    • [Paper]

  • Learning Multiple-Question Decision Trees for Cold-Start Recommendation (WSDM 2013)

    • Mingxuan Sun, Fuxin Li, Joonseok Lee, Ke Zhou, Guy Lebanon, Hongyuan Zha
    • [Paper]

2013

  • Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria (ICCV 2013)

    • Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht
    • [Paper]

  • Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees (ICCV 2013)

    • Oisin Mac Aodha, Gabriel J. Brostow
    • [Paper]

  • Conformal Prediction Using Decision Trees (ICDM 2013)

    • Ulf Johansson, Henrik Boström, Tuve Löfström
    • [Paper]

  • Focal-Test-Based Spatial Decision Tree Learning: A Summary of Results (ICDM 2013)

    • Zhe Jiang, Shashi Shekhar, Xun Zhou, Joseph K. Knight, Jennifer Corcoran
    • [Paper]

  • Top-down Particle Filtering for Bayesian Decision Trees (ICML 2013)

    • Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
    • [Paper]

  • Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)

    • Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona
    • [Paper]

  • Knowledge Compilation for Model Counting: Affine Decision Trees (IJCAI 2013)

    • Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas
    • [Paper]

  • Understanding Variable Importances in Forests of Randomized Trees (NIPS 2013)

    • Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
    • [Paper]

  • Regression-tree Tuning in a Streaming Setting (NIPS 2013)

    • Samory Kpotufe, Francesco Orabona
    • [Paper]

  • Learning Max-Margin Tree Predictors (UAI 2013)

    • Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson
    • [Paper]

2012

  • Regression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems (CVPR 2012)

    • Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, Carsten Rother
    • [Paper]

  • ConfDTree: Improving Decision Trees Using Confidence Intervals (ICDM 2012)

    • Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek
    • [Paper]

  • Improved Information Gain Estimates for Decision Tree Induction (ICML 2012)

  • Learning Partially Observable Models Using Temporally Abstract Decision Trees (NIPS 2012)

  • Subtree Replacement in Decision Tree Simplification (SDM 2012)

2011

  • Incorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)

    • Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
    • [Paper]

  • Syntactic Decision Tree LMs: Random Selection or Intelligent Design (EMNLP 2011)

    • Denis Filimonov, Mary P. Harper
    • [Paper]

  • Decision Tree Fields (ICCV 2011)

    • Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli
    • [Paper]

  • Confidence in Predictions from Random Tree Ensembles (ICDM 2011)

  • Speeding-Up Hoeffding-Based Regression Trees With Options (ICML 2011)

    • Elena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski
    • [Paper]

  • Density Estimation Trees (KDD 2011)

    • Parikshit Ram, Alexander G. Gray
    • [Paper]

  • Bagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)

    • Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes
    • [Paper]

  • On the Complexity of Decision Making in Possibilistic Decision Trees (UAI 2011)

    • Hélène Fargier, Nahla Ben Amor, Wided Guezguez
    • [Paper]

  • Adaptive Bootstrapping of Recommender Systems Using Decision Trees (WSDM 2011)

    • Nadav Golbandi, Yehuda Koren, Ronny Lempel
    • [Paper]

  • Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)

    • Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin
    • [Paper]

2010

  • Discrimination Aware Decision Tree Learning (ICDM 2010)

    • Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
    • [Paper]

  • Decision Trees for Uplift Modeling (ICDM 2010)

    • Piotr Rzepakowski, Szymon Jaroszewicz
    • [Paper]

  • Learning Markov Network Structure with Decision Trees (ICDM 2010)

  • Multivariate Dyadic Regression Trees for Sparse Learning Problems (NIPS 2010)

  • Fast and Accurate Gene Prediction by Decision Tree Classification (SDM 2010)

    • Rong She, Jeffrey Shih-Chieh Chu, Ke Wang, Nansheng Chen
    • [Paper]

  • A Robust Decision Tree Algorithm for Imbalanced Data Sets (SDM 2010)

    • Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla
    • [Paper]

2009

  • Stochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)

    • Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng
    • [Paper]

  • Feature Selection for Ranking Using Boosted Trees (CIKM 2009)

    • Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato
    • [Paper]

  • Thai Word Segmentation with Hidden Markov Model and Decision Tree (PAKDD 2009)

    • Poramin Bheganan, Richi Nayak, Yue Xu
    • [Paper]

  • Parameter Estimdation in Semi-Random Decision Tree Ensembling on Streaming Data (PAKDD 2009)

    • Pei-Pei Li, Qianhui Liang, Xindong Wu, Xuegang Hu
    • [Paper]

  • DTU: A Decision Tree for Uncertain Data (PAKDD 2009)

    • Biao Qin, Yuni Xia, Fang Li
    • [Paper]

2008

  • Predicting Future Decision Trees from Evolving Data (ICDM 2008)

    • Mirko Böttcher, Martin Spott, Rudolf Kruse
    • [Paper]

  • Bayes Optimal Classification for Decision Trees (ICML 2008)

  • A New Credit Scoring Method Based on Rough Sets and Decision Tree (PAKDD 2008)

    • XiYue Zhou, Defu Zhang, Yi Jiang
    • [Paper]

  • A Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees (PAKDD 2008)

    • Philippe Lenca, Stéphane Lallich, Thanh-Nghi Do, Nguyen-Khang Pham
    • [Paper]

  • BOAI: Fast Alternating Decision Tree Induction Based on Bottom-Up Evaluation (PAKDD 2008)

    • Bishan Yang, Tengjiao Wang, Dongqing Yang, Lei Chang
    • [Paper]

  • A General Framework for Estimating Similarity of Datasets and Decision Trees: Exploring Semantic Similarity of Decision Trees (SDM 2008)

    • Irene Ntoutsi, Alexandros Kalousis, Yannis Theodoridis
    • [Paper]

  • ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data (SDM 2008)

    • M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
    • [Paper]

2007

  • Tree-based Classifiers for Bilayer Video Segmentation (CVPR 2007)

    • Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
    • [Paper]

  • Additive Groves of Regression Trees (ECML 2007)

    • Daria Sorokina, Rich Caruana, Mirek Riedewald
    • [Paper]

  • Decision Tree Instability and Active Learning (ECML 2007)

    • Kenneth Dwyer, Robert Holte
    • [Paper]

  • Ensembles of Multi-Objective Decision Trees (ECML 2007)

    • Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski
    • [Paper]

  • Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble (ECML 2007)

    • Anneleen Van Assche, Hendrik Blockeel
    • [Paper]

  • Sample Compression Bounds for Decision Trees (ICML 2007)

  • A Tighter Error Bound for Decision Tree Learning Using PAC Learnability (IJCAI 2007)

    • Chaithanya Pichuka, Raju S. Bapi, Chakravarthy Bhagvati, Arun K. Pujari, Bulusu Lakshmana Deekshatulu
    • [Paper]

  • Keep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary (IJCAI 2007)

    • Isabelle Alvarez, Stephan Bernard, Guillaume Deffuant
    • [Paper]

  • Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)

    • Claudia Henry, Richard Nock, Frank Nielsen
    • [Paper]

  • Scalable Look-ahead Linear Regression Trees (KDD 2007)

    • David S. Vogel, Ognian Asparouhov, Tobias Scheffer
    • [Paper]

  • Mining Optimal Decision Trees from Itemset Lattices (KDD 2007)

    • Siegfried Nijssen, Élisa Fromont
    • [Paper]

  • A Hybrid Multi-group Privacy-Preserving Approach for Building Decision Trees (PAKDD 2007)

    • Zhouxuan Teng, Wenliang Du
    • [Paper]

2006

  • Decision Tree Methods for Finding Reusable MDP Homomorphisms (AAAI 2006)

    • Alicia P. Wolfe, Andrew G. Barto
    • [Paper]

  • A Fast Decision Tree Learning Algorithm (AAAI 2006)

  • Anytime Induction of Decision Trees: An Iterative Improvement Approach (AAAI 2006)

    • Saher Esmeir, Shaul Markovitch
    • [Paper]

  • When a Decision Tree Learner Has Plenty of Time (AAAI 2006)

    • Saher Esmeir, Shaul Markovitch
    • [Paper]

  • Decision Trees for Functional Variables (ICDM 2006)

    • Suhrid Balakrishnan, David Madigan
    • [Paper]

  • Cost-Sensitive Decision Tree Learning for Forensic Classification (ECML 2006)

    • Jason V. Davis, Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, Emmett Witchel
    • [Paper]

  • Improving the Ranking Performance of Decision Trees (ECML 2006)

  • A General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees (KDD 2006)

    • Wei Fan, Joe McCloskey, Philip S. Yu
    • [Paper]

  • Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction (PAKDD 2006)

    • Phu Chien Nguyen, Kouzou Ohara, Akira Mogi, Hiroshi Motoda, Takashi Washio
    • [Paper]

  • Variable Randomness in Decision Tree Ensembles (PAKDD 2006)

    • Fei Tony Liu, Kai Ming Ting
    • [Paper]

  • Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees (PAKDD 2006)

    • Dan A. Simovici, Szymon Jaroszewicz
    • [Paper]

  • Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics (PKDD 2006)

    • Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saso Dzeroski, Amanda Clare
    • [Paper]

  • k-Anonymous Decision Tree Induction (PKDD 2006)

    • Arik Friedman, Assaf Schuster, Ran Wolff
    • [Paper]

2005

  • Representing Conditional Independence Using Decision Trees (AAAI 2005)

  • Use of Expert Knowledge for Decision Tree Pruning (AAAI 2005)

    • Jingfeng Cai, John Durkin
    • [Paper]

  • Model Selection in Omnivariate Decision Trees (ECML 2005)

    • Olcay Taner Yildiz, Ethem Alpaydin
    • [Paper]

  • Combining Bias and Variance Reduction Techniques for Regression Trees (ECML 2005)

    • Yuk Lai Suen, Prem Melville, Raymond J. Mooney
    • [Paper]

  • Simple Test Strategies for Cost-Sensitive Decision Trees (ECML 2005)

    • Shengli Sheng, Charles X. Ling, Qiang Yang
    • [Paper]

  • Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches (ICDM 2005)

    • Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Yu, Kevin Drummey
    • [Paper]

  • Exploiting Informative Priors for Bayesian Classification and Regression Trees (IJCAI 2005)

    • Nicos Angelopoulos, James Cussens
    • [Paper]

  • Ranking Cases with Decision Trees: a Geometric Method that Preserves Intelligibility (IJCAI 2005)

    • Isabelle Alvarez, Stephan Bernard
    • [Paper]

  • Maximizing Tree Diversity by Building Complete-Random Decision Trees (PAKDD 2005)

    • Fei Tony Liu, Kai Ming Ting, Wei Fan
    • [Paper]

  • Hybrid Cost-Sensitive Decision Tree (PKDD 2005)

    • Shengli Sheng, Charles X. Ling
    • [Paper]

  • Tree2 - Decision Trees for Tree Structured Data (PKDD 2005)

    • Björn Bringmann, Albrecht Zimmermann
    • [Paper]

  • Building Decision Trees on Records Linked through Key References (SDM 2005)

    • Ke Wang, Yabo Xu, Philip S. Yu, Rong She
    • [Paper]

  • Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases (SDM 2005)

    • Amir Bar-Or, Ran Wolff, Assaf Schuster, Daniel Keren
    • [Paper]

  • Boosted Decision Trees for Word Recognition in Handwritten Document Retrieval (SIGIR 2005)

    • Nicholas R. Howe, Toni M. Rath, R. Manmatha
    • [Paper]

2004

  • On the Optimality of Probability Estimation by Random Decision Trees (AAAI 2004)

  • Occam's Razor and a Non-Syntactic Measure of Decision Tree Complexity (AAAI 2004)

  • Using Emerging Patterns and Decision Trees in Rare-Class Classification (ICDM 2004)

    • Hamad Alhammady, Kotagiri Ramamohanarao
    • [Paper]

  • Orthogonal Decision Trees (ICDM 2004)

    • Hillol Kargupta, Haimonti Dutta
    • [Paper]

  • Improving the Reliability of Decision Tree and Naive Bayes Learners (ICDM 2004)

    • David George Lindsay, Siân Cox
    • [Paper]

  • Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data (ICDM 2004)

    • Chris Giannella, Kun Liu, Todd Olsen, Hillol Kargupta
    • [Paper]

  • Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams (ICDM 2004)

    • Wei Fan, Yi-an Huang, Philip S. Yu
    • [Paper]

  • Lookahead-based Algorithms for Anytime Induction of Decision Trees (ICML 2004)

    • Saher Esmeir, Shaul Markovitch
    • [Paper]

  • Decision Trees with Minimal Costs (ICML 2004)

    • Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang
    • [Paper]

  • Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)

    • Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov
    • [Paper]

  • Detecting Structural Metadata with Decision Trees and Transformation-Based Learning (NAACL 2004)

    • Joungbum Kim, Sarah E. Schwarm, Mari Ostendorf
    • [Paper]

  • On the Adaptive Properties of Decision Trees (NIPS 2004)

    • Clayton D. Scott, Robert D. Nowak
    • [Paper]

  • A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index (PAKDD 2004)

    • Dan A. Simovici, Szymon Jaroszewicz
    • [Paper]

2003

  • Rademacher Penalization over Decision Tree Prunings (ECML 2003)

    • Matti Kääriäinen, Tapio Elomaa
    • [Paper]

  • Ensembles of Cascading Trees (ICDM 2003)

  • Postprocessing Decision Trees to Extract Actionable Knowledge (ICDM 2003)

    • Qiang Yang, Jie Yin, Charles X. Ling, Tielin Chen
    • [Paper]

  • K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier (ICDM 2003)

    • Tomoyuki Shibata, Takekazu Kato, Toshikazu Wada
    • [Paper]

  • Identifying Markov Blankets with Decision Tree Induction (ICDM 2003)

    • Lewis J. Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov
    • [Paper]

  • Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy (ICDM 2003)

    • Jin Huang, Jingjing Lu, Charles X. Ling
    • [Paper]

  • Boosting Lazy Decision Trees (ICML 2003)

    • Xiaoli Zhang Fern, Carla E. Brodley
    • [Paper]

  • Decision Tree with Better Ranking (ICML 2003)

    • Charles X. Ling, Robert J. Yan
    • [Paper]

  • Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction (IJCAI 2003)

  • Efficient Decision Tree Construction on Streaming Data (KDD 2003)

    • Ruoming Jin, Gagan Agrawal
    • [Paper]

  • PaintingClass: Interactive Construction Visualization and Exploration of Decision Trees (KDD 2003)

    • Soon Tee Teoh, Kwan-Liu Ma
    • [Paper]

  • Accurate Decision Trees for Mining High-Speed Data Streams (KDD 2003)

    • João Gama, Ricardo Rocha, Pedro Medas
    • [Paper]

  • Near-Minimax Optimal Classification with Dyadic Classification Trees (NIPS 2003)

    • Clayton D. Scott, Robert D. Nowak
    • [Paper]

  • Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule (PAKDD 2003)

    • Chenzhou Ye, Jie Yang, Lixiu Yao, Nian-yi Chen
    • [Paper]

  • Arbogodai: a New Approach for Decision Trees (PKDD 2003)

    • Djamel A. Zighed, Gilbert Ritschard, Walid Erray, Vasile-Marian Scuturici
    • [Paper]

  • Communication and Memory Efficient Parallel Decision Tree Construction (SDM 2003)

    • Ruoming Jin, Gagan Agrawal
    • [Paper]

  • Decision Tree Classification of Spatial Data Patterns from Videokeratography using Zernicke Polynomials (SDM 2003)

    • Michael D. Twa, Srinivasan Parthasarathy, Thomas W. Raasch, Mark Bullimore
    • [Paper]

2002

  • Multiclass Alternating Decision Trees (ECML 2002)

    • Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark A. Hall
    • [Paper]

  • Heterogeneous Forests of Decision Trees (ICANN 2002)

    • Krzysztof Grabczewski, Wlodzislaw Duch
    • [Paper]

  • Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)

  • Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)

  • Learning Decision Trees Using the Area Under the ROC Curve (ICML 2002)

    • César Ferri, Peter A. Flach, José Hernández-Orallo
    • [Paper]

  • Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction (ICML 2002)

    • Fumio Takechi, Einoshin Suzuki
    • [Paper]

  • Efficiently Mining Frequent Trees in a Forest (KDD 2002)

  • SECRET: a Scalable Linear Regression Tree Algorithm (KDD 2002)

    • Alin Dobra, Johannes Gehrke
    • [Paper]

  • Instability of Decision Tree Classification Algorithms (KDD 2002)

    • Ruey-Hsia Li, Geneva G. Belford
    • [Paper]

  • Extracting Decision Trees From Trained Neural Networks (KDD 2002)

  • Dyadic Classification Trees via Structural Risk Minimization (NIPS 2002)

    • Clayton D. Scott, Robert D. Nowak
    • [Paper]

  • Approximate Splitting for Ensembles of Trees using Histograms (SDM 2002)

    • Chandrika Kamath, Erick Cantú-Paz, David Littau
    • [Paper]

2001

  • Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning (ACL 2001)

  • Message Length as an Effective Ockham's Razor in Decision Tree Induction (AISTATS 2001)

    • Scott Needham, David L. Dowe
    • [Paper]

  • SQL Database Primitives for Decision Tree Classifiers (CIKM 2001)

    • Kai-Uwe Sattler, Oliver Dunemann
    • [Paper]

  • A Unified Framework for Evaluation Metrics in Classification Using Decision Trees (ECML 2001)

    • Ricardo Vilalta, Mark Brodie, Daniel Oblinger, Irina Rish
    • [Paper]

  • Backpropagation in Decision Trees for Regression (ECML 2001)

    • Victor Medina-Chico, Alberto Suárez, James F. Lutsko
    • [Paper]

  • Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction (ECML 2001)

    • Branko Kavsek, Nada Lavrac, Anuska Ferligoj
    • [Paper]

  • Mining Decision Trees from Data Streams in a Mobile Environment (ICDM 2001)

    • Hillol Kargupta, Byung-Hoon Park
    • [Paper]

  • Efficient Determination of Dynamic Split Points in a Decision Tree (ICDM 2001)

    • David Maxwell Chickering, Christopher Meek, Robert Rounthwaite
    • [Paper]

  • A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)

    • Bernard Zenko, Ljupco Todorovski, Saso Dzeroski
    • [Paper]

  • Efficient Algorithms for Decision Tree Cross-Validation (ICML 2001)

    • Hendrik Blockeel, Jan Struyf
    • [Paper]

  • Bias Correction in Classification Tree Construction (ICML 2001)

    • Alin Dobra, Johannes Gehrke
    • [Paper]

  • Breeding Decision Trees Using Evolutionary Techniques (ICML 2001)

    • Athanassios Papagelis, Dimitrios Kalles
    • [Paper]

  • Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers (ICML 2001)

    • Bianca Zadrozny, Charles Elkan
    • [Paper]

  • Temporal Decision Trees or the lazy ECU vindicated (IJCAI 2001)

    • Luca Console, Claudia Picardi, Daniele Theseider Dupré
    • [Paper]

  • Data Mining Criteria for Tree-based Regression and Classification (KDD 2001)

    • Andreas Buja, Yung-Seop Lee
    • [Paper]

  • A Decision Tree of Bigrams is an Accurate Predictor of Word Sense (NAACL 2001)

  • Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron (PAKDD 2001)

  • A Scalable Algorithm for Rule Post-pruning of Large Decision Trees (PAKDD 2001)

    • Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira
    • [Paper]

  • Optimizing the Induction of Alternating Decision Trees (PAKDD 2001)

    • Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby
    • [Paper]

  • Interactive Construction of Decision Trees (PAKDD 2001)

    • Jianchao Han, Nick Cercone
    • [Paper]

  • Bloomy Decision Tree for Multi-objective Classification (PKDD 2001)

    • Einoshin Suzuki, Masafumi Gotoh, Yuta Choki
    • [Paper]

  • A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment (SDM 2001)

    • Byung-Hoon Park, Rajeev Ayyagari, Hillol Kargupta
    • [Paper]

2000

  • Intuitive Representation of Decision Trees Using General Rules and Exceptions (AAAI 2000)

    • Bing Liu, Minqing Hu, Wynne Hsu
    • [Paper]

  • Tagging Unknown Proper Names Using Decision Trees (ACL 2000)

    • Frédéric Béchet, Alexis Nasr, Franck Genet
    • [Paper]

  • Clustering Through Decision Tree Construction (CIKM 2000)

    • Bing Liu, Yiyuan Xia, Philip S. Yu
    • [Paper]

  • Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling (ECML 2000)

  • Investigation and Reduction of Discretization Variance in Decision Tree Induction (ECML 2000)

    • Pierre Geurts, Louis Wehenkel
    • [Paper]

  • Nonparametric Regularization of Decision Trees (ECML 2000)

  • Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (ICML 2000)

    • Chris Drummond, Robert C. Holte
    • [Paper]

  • Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (ICML 2000)

    • Manu Sridharan, Gerald Tesauro
    • [Paper]

  • Growing Decision Trees on Support-less Association Rules (KDD 2000)

    • Ke Wang, Senqiang Zhou, Yu He
    • [Paper]

  • Efficient Algorithms for Constructing Decision Trees with Constraints (KDD 2000)

    • Minos N. Garofalakis, Dongjoon Hyun, Rajeev Rastogi, Kyuseok Shim
    • [Paper]

  • Interactive Visualization in Mining Large Decision Trees (PAKDD 2000)

    • Trong Dung Nguyen, Tu Bao Ho, Hiroshi Shimodaira
    • [Paper]

  • VQTree: Vector Quantization for Decision Tree Induction (PAKDD 2000)

    • Shlomo Geva, Lawrence Buckingham
    • [Paper]

  • Some Enhencements of Decision Tree Bagging (PKDD 2000)

  • Combining Multiple Models with Meta Decision Trees (PKDD 2000)

    • Ljupco Todorovski, Saso Dzeroski
    • [Paper]

  • Induction of Multivariate Decision Trees by Using Dipolar Criteria (PKDD 2000)

    • Leon Bobrowski, Marek Kretowski
    • [Paper]

  • Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms (PKDD 2000)

    • Nikos Drossos, Athanassios Papagelis, Dimitrios Kalles
    • [Paper]

1999

  • Modeling Decision Tree Performance with the Power Law (AISTATS 1999)

    • Lewis J. Frey, Douglas H. Fisher
    • [Paper]

  • Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens (AISTATS 1999)

  • POS Tags and Decision Trees for Language Modeling (EMNLP 1999)

  • Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)

    • Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
    • [Paper]

  • The Alternating Decision Tree Learning Algorithm (ICML 1999)

  • Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)

    • Yishay Mansour, David A. McAllester
    • [Paper]

1998

  • Learning Sorting and Decision Trees with POMDPs (ICML 1998)

    • Blai Bonet, Hector Geffner
    • [Paper]

  • Using a Permutation Test for Attribute Selection in Decision Trees (ICML 1998)

    • Eibe Frank, Ian H. Witten
    • [Paper]

  • A Fast and Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (ICML 1998)

    • Michael J. Kearns, Yishay Mansour
    • [Paper]

1997

  • Pessimistic Decision Tree Pruning Based Continuous-Time (ICML 1997)

  • PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (ICML 1997)

  • Option Decision Trees with Majority Votes (ICML 1997)

  • Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction (ICML 1997)

    • Ricardo Vilalta, Larry A. Rendell
    • [Paper]

  • Functional Models for Regression Tree Leaves (ICML 1997)

  • The Effects of Training Set Size on Decision Tree Complexity (ICML 1997)

    • Tim Oates, David D. Jensen
    • [Paper]

  • Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis (NIPS 1997)

    • Marcus Held, Joachim M. Buhmann
    • [Paper]

  • Data-Dependent Structural Risk Minimization for Perceptron Decision Trees (NIPS 1997)

    • John Shawe-Taylor, Nello Cristianini
    • [Paper]

  • Generalization in Decision Trees and DNF: Does Size Matter (NIPS 1997)

    • Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason
    • [Paper]

1996

  • Second Tier for Decision Trees (ICML 1996)

  • Non-Linear Decision Trees - NDT (ICML 1996)

    • Andreas Ittner, Michael Schlosser
    • [Paper]

  • Learning Relational Concepts with Decision Trees (ICML 1996)

    • Peter Geibel, Fritz Wysotzki
    • [Paper]

1995

  • A Hill-Climbing Approach for Optimizing Classification Trees (AISTATS 1995)

    • Xiaorong Sun, Steve Y. Chiu, Louis Anthony Cox Jr.
    • [Paper]

  • An Exact Probability Metric for Decision Tree Splitting (AISTATS 1995)

  • On Pruning and Averaging Decision Trees (ICML 1995)

    • Jonathan J. Oliver, David J. Hand
    • [Paper]

  • On Handling Tree-Structured Attributed in Decision Tree Learning (ICML 1995)

    • Hussein Almuallim, Yasuhiro Akiba, Shigeo Kaneda
    • [Paper]

  • Retrofitting Decision Tree Classifiers Using Kernel Density Estimation (ICML 1995)

    • Padhraic Smyth, Alexander G. Gray, Usama M. Fayyad
    • [Paper]

  • Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves (ICML 1995)

  • Efficient Algorithms for Finding Multi-way Splits for Decision Trees (ICML 1995)

    • Truxton Fulton, Simon Kasif, Steven Salzberg
    • [Paper]

  • Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (ICML 1995)

    • Peter Auer, Robert C. Holte, Wolfgang Maass
    • [Paper]

  • Boosting Decision Trees (NIPS 1995)

    • Harris Drucker, Corinna Cortes
    • [Paper]

  • Using Pairs of Data-Points to Define Splits for Decision Trees (NIPS 1995)

    • Geoffrey E. Hinton, Michael Revow
    • [Paper]

  • A New Pruning Method for Solving Decision Trees and Game Trees (UAI 1995)

1994

  • A Statistical Approach to Decision Tree Modeling (ICML 1994)

  • In Defense of C4.5: Notes Learning One-Level Decision Trees (ICML 1994)

  • An Improved Algorithm for Incremental Induction of Decision Trees (ICML 1994)

  • Decision Tree Parsing using a Hidden Derivation Model (NAACL 1994)

    • Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer, Adwait Ratnaparkhi, Salim Roukos
    • [Paper]

1993

  • Using Decision Trees to Improve Case-Based Learning (ICML 1993)

1991

  • Context Dependent Modeling of Phones in Continuous Speech Using Decision Trees (NAACL 1991)
    • Lalit R. Bahl, Peter V. de Souza, P. S. Gopalakrishnan, David Nahamoo, Michael Picheny
    • [Paper]

1989

  • Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications (NIPS 1989)
    • Les E. Atlas, Ronald A. Cole, Jerome T. Connor, Mohamed A. El-Sharkawi, Robert J. Marks II, Yeshwant K. Muthusamy, Etienne Barnard
    • [Paper]

1988

  • Multiple Decision Trees (UAI 1988)
    • Suk Wah Kwok, Chris Carter
    • [Paper]

1987

  • Decision Tree Induction Systems: A Bayesian Analysis (UAI 1987)

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