benedekrozemberczki / awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

Awesome Decision, Classification, and Regression Tree Research Papers

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

• Machine learning
  • NeurIPS
  • ICML
  • ICLR
• Computer vision
  • CVPR
  • ICCV
  • ECCV
• Natural language processing
  • ACL
  • NAACL
  • EMNLP
• Data
  • KDD
  • CIKM
  • ICDM
  • SDM
  • PAKDD
  • PKDD/ECML
  • SIGIR
  • WWW
  • WSDM
• Artificial intelligence
  • AAAI
  • AISTATS
  • ICANN
  • IJCAI
  • UAI

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

2022

• Using MaxSAT for Efficient Explanations of Tree Ensembles (AAAI 2022)

  • Alexey Ignatiev, Yacine Izza, Peter J. Stuckey, João Marques-Silva
  • [Paper]

• FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles (AAAI 2022)

  • Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke
  • [Paper]

• Explainable and Local Correction of Classification Models Using Decision Trees (AAAI 2022)

  • Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Keisuke Goto, Yuta Fujishige, Satoshi Hara
  • [Paper]

• Robust Optimal Classification Trees against Adversarial Examples (AAAI 2022)

  • Daniël Vos, Sicco Verwer
  • [Paper]

• Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values (AAAI 2022)

  • Haewon Jeong, Hao Wang, Flávio P. Calmon
  • [Paper]

• Fast Sparse Decision Tree Optimization via Reference Ensembles (AAAI 2022)

  • Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer
  • [Paper]
  • [Code]

• TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)

  • Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu
  • [Paper]
  • [Code]

• Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees (AISTATS 2022)

  • Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike
  • [Paper]

• Accurate Shapley Values for explaining tree-based models (AISTATS 2022)

  • Salim I. Amoukou, Tangi Salaün, Nicolas J.-B. Brunel
  • [Paper]

• A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds (AISTATS 2022)

  • Yan Shuo Tan, Abhineet Agarwal, Bin Yu
  • [Paper]
  • [Code]

• Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees (WWW 2022)

  • Philip A. Etter, Kai Zhong, Hsiang-Fu Yu, Lexing Ying, Inderjit S. Dhillon
  • [Paper]

• MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration (WWW 2022)

  • Siguang Huang, Yunli Wang, Lili Mou, Huayue Zhang, Han Zhu, Chuan Yu, Bo Zheng
  • [Paper]

• Rethinking Conversational Recommendations: Is Decision Tree All You Need (CIKM 2022)

  • A S. M. Ahsan-Ul-Haque, Hongning Wang
  • [Paper]

• A Neural Tangent Kernel Perspective of Infinite Tree Ensembles (ICLR 2022)

  • Ryuichi Kanoh, Mahito Sugiyama
  • [Paper]

• POETREE: Interpretable Policy Learning with Adaptive Decision Trees (ICLR 2022)

  • Alizée Pace, Alex Chan, Mihaela van der Schaar
  • [Paper]

• Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models (ICML 2022)

  • Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu
  • [Paper]

• Popular decision tree algorithms are provably noise tolerant (ICML 2022)

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

• Robust Counterfactual Explanations for Tree-Based Ensembles (ICML 2022)

  • Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni
  • [Paper]

• Fast Provably Robust Decision Trees and Boosting (ICML 2022)

  • Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou
  • [Paper]

• BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression (ICML 2022)

  • Zhao Tang Luo, Huiyan Sang, Bani K. Mallick
  • [Paper]

• Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features (ICML 2022)

  • Rahul Mazumder, Xiang Meng, Haoyue Wang
  • [Paper]

• A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources (ICML 2022)

  • Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang
  • [Paper]

• On Preferred Abductive Explanations for Decision Trees and Random Forests (IJCAI 2022)

  • Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis
  • [Paper]

• Extending Decision Tree to Handle Multiple Fairness Criteria (IJCAI 2022)

  • Alessandro Castelnovo
  • [Paper]

• Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles (KDD 2022)

  • Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder
  • [Paper]

• Integrity Authentication in Tree Models (KDD 2022)

  • Weijie Zhao, Yingjie Lao, Ping Li
  • [Paper]

• Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)

  • Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu
  • [Paper]

• Improved feature importance computation for tree models based on the Banzhaf value (UAI 2022)

  • Adam Karczmarz, Tomasz Michalak, Anish Mukherjee, Piotr Sankowski, Piotr Wygocki
  • [Paper]

• Learning linear non-Gaussian polytree models (UAI 2022)

  • Daniele Tramontano, Anthea Monod, Mathias Drton
  • [Paper]

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)

  • Florent Avellaneda
  • [Paper]
  • [Code]

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

  • Gael Aglin, Siegfried Nijssen, Pierre Schaus
  • [Paper]
  • [Code]

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

  • Francesco Ranzato, Marco Zanella
  • [Paper]
  • [Code]

• 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)

  • Brian Lucena
  • [Paper]

• 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)

  • Guang-He Lee, Tommi S. Jaakkola
  • [Paper]
  • [Code]

• 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)

  • Thibaut Vidal, Maximilian Schiffer
  • [Paper]
  • [Code]

• 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)

  • Gaël Aglin, Siegfried Nijssen, Pierre Schaus
  • [Paper]
  • [Code]

• 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)

  • Chong Zhang, Huan Zhang, Cho-Jui Hsieh
  • [Paper]
  • [Code]

• 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)

  • Ya-Lin Zhang, Longfei Li
  • [Paper]

• 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)

  • Jaemin Yoo, Lee Sael
  • [Paper]
  • [Code]

• 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)

  • Wenbin Zhang, Eirini Ntoutsi
  • [Paper]
  • [Code]

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

  • Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib
  • [Paper]
  • [Matlab Code]
  • [R Code]

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

  • Yu Shi, Jian Li, Zhize Li
  • [Paper]
  • [Code]

• 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)

  • Maksym Andriushchenko, Matthias Hein
  • [Paper]
  • [Code]

• 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)

  • Xiyang Hu, Cynthia Rudin, Margo Seltzer
  • [Paper]
  • [Code]

• MonoForest framework for tree ensemble analysis (NeurIPS 2019)

  • Igor Kuralenok, Vasilii Ershov, Igor Labutin
  • [Paper]
  • [Code]

• 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)

  • Shlomi Maliah, Guy Shani
  • [Paper]

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

  • Takuhiro Kaneko, Kaoru Hiramatsu, Kunio Kashino
  • [Paper]
  • [Code]

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

  • Viktor Losing, Heiko Wersing, Barbara Hammer
  • [Paper]
  • [Code]

• 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)

  • Ji Feng, Yang Yu, Zhi-Hua Zhou
  • [Paper]
  • [Code]

• 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)

  • Salvatore Ruggieri
  • [Paper]

• 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)

  • Scott M. Lundberg, Su-In Lee
  • [Paper]
  • [Code]

• 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)

  • Scott M. Lundberg, Su-In Lee
  • [Paper]
  • [Code]

• 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)

  • Lixin Fan
  • [Paper]

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

  • Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
  • [Paper]

• Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)

  • Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov
  • [Paper]

• XGBoost: A Scalable Tree Boosting System (KDD 2016)

  • Tianqi Chen, Carlos Guestrin
  • [Paper]
  • [Code]

• 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]
  • [Code]

• 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
  • [Paper]
  • [Code]

• Post-Learning Optimization of Tree Ensembles for Efficient Ranking (SIGIR 2016)

  • Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, Salvatore Trani
  • [Paper]
  • [Code]

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)

  • Korlakai Vinayak Rashmi, Ran Gilad-Bachrach
  • [Paper]
  • [Code]

• 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)

  • Donghoon Lee, Hyunsin Park, Chang Dong Yoo
  • [Paper]
  • [Code]

• 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)

  • Balázs Kégl
  • [Paper]

• 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)

  • Sebastian Nowozin
  • [Paper]

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

  • Erik Talvitie
  • [Paper]

• Subtree Replacement in Decision Tree Simplification (SDM 2012)

  • Salvatore Ruggieri
  • [Paper]

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)

  • Siddhartha Bhattacharyya
  • [Paper]

• 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)

  • Daniel Lowd, Jesse Davis
  • [Paper]

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

  • Han Liu, Xi Chen
  • [Paper]

• 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)

  • Siegfried Nijssen
  • [Paper]

• 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)

  • Mohak Shah
  • [Paper]

• 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)

  • Jiang Su, Harry Zhang
  • [Paper]

• 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)

  • Bin Wang, Harry Zhang
  • [Paper]

• 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)

  • Jiang Su, Harry Zhang
  • [Paper]

• 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)

  • Wei Fan
  • [Paper]

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

  • Goutam Paul
  • [Paper]

• 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)

  • Jinyan Li, Huiqing Liu
  • [Paper]

• 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)

  • David Page, Soumya Ray
  • [Paper]

• 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)

  • Jinyan Li, Limsoon Wong
  • [Paper]

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

  • Jinyan Li, Limsoon Wong
  • [Paper]

• 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)

  • Mohammed Javeed Zaki
  • [Paper]

• 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)

  • Olcay Boz
  • [Paper]

• 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)

  • Hideki Isozaki
  • [Paper]

• 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)

  • Ted Pedersen
  • [Paper]

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

  • DaeEun Kim, Jaeho Lee
  • [Paper]

• 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)

  • DaeEun Kim, Jaeho Lee
  • [Paper]

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

  • Pierre Geurts, Louis Wehenkel
  • [Paper]

• Nonparametric Regularization of Decision Trees (ECML 2000)

  • Tobias Scheffer
  • [Paper]

• 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)

  • Pierre Geurts
  • [Paper]

• 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)

  • Louis Anthony Cox Jr.
  • [Paper]

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

  • Peter A. Heeman
  • [Paper]

• 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)

  • Yoav Freund, Llew Mason
  • [Paper]
  • [Code]

• 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)

  • Yishay Mansour
  • [Paper]

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

  • Scott E. Decatur
  • [Paper]

• Option Decision Trees with Majority Votes (ICML 1997)

  • Ron Kohavi, Clayton Kunz
  • [Paper]

• 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)

  • Luís Torgo
  • [Paper]

• 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)

  • Miroslav Kubat
  • [Paper]

• 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)

  • J. Kent Martin
  • [Paper]

• 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)

  • David J. Lubinsky
  • [Paper]

• 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)

  • Prakash P. Shenoy
  • [Paper]

1994

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

  • Michael I. Jordan
  • [Paper]

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

  • Tapio Elomaa
  • [Paper]

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

  • Paul E. Utgoff
  • [Paper]

• 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)
  • Claire Cardie
  • [Paper]

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)
  • Wray L. Buntine
  • [Paper]

---

License

• CC0 Universal