Awesome Decision Tree Research Papers
A collection of research papers on decision, classification and regression trees with implementations.
Under Creative Commons Zero v1.0 Universal
By benedekrozemberczki
A collection of research papers on decision, classification and regression trees with implementations.
Under Creative Commons Zero v1.0 Universal
By benedekrozemberczki
Awesome Decision Tree Research Papers
⠀
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.
[Code]
Optimal Decision Trees for Nonlinear Metrics (AAAI 2021)
[Paper]
SAT-based Decision Tree Learning for Large Data Sets (AAAI 2021)
[Paper]
Parameterized Complexity of Small Decision Tree Learning (AAAI 2021)
[Paper]
Counterfactual Explanations for Oblique Decision Trees: Exact - Efficient Algorithms (AAAI 2021)
[Paper]
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)
[Paper]
NBDT: Neural-Backed Decision Tree (ICLR 2021)
[Paper]
Versatile Verification of Tree Ensembles (ICML 2021)
[Paper]
Connecting Interpretability and Robustness in Decision Trees through Separation (ICML 2021)
[Paper]
Optimal Counterfactual Explanations in Tree Ensembles (ICML 2021)
[Paper]
Efficient Training of Robust Decision Trees Against Adversarial Examples (ICML 2021)
[Paper]
Learning Binary Decision Trees by Argmin Differentiation (ICML 2021)
[Paper]
BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment (KDD 2021)
[Paper]
ControlBurn: Feature Selection by Sparse Forests (KDD 2021)
[Paper]
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)
[Paper]
Verifying Tree Ensembles by Reasoning about Potential Instances (SDM 2021)
[Paper]
Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)
[Paper]
Practical Federated Gradient Boosting Decision Trees (AAAI 2020)
[Paper]
Efficient Inference of Optimal Decision Trees (AAAI 2020)
[Code]
Learning Optimal Decision Trees Using Caching Branch-and-Bound Search (AAAI 2020)
[Code]
Abstract Interpretation of Decision Tree Ensemble Classifiers (AAAI 2020)
[Code]
Scalable Feature Selection for (Multitask) Gradient Boosted Trees (AISTATS 2020)
[Paper]
Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning (AISTATS 2020)
[Paper]
Exploiting Categorical Structure Using Tree-Based Methods (AISTATS 2020)
[Paper]
LdSM: Logarithm-depth Streaming Multi-label Decision Trees (AISTATS 2020)
[Paper]
Oblique Decision Trees from Derivatives of ReLU Networks (ICLR 2020)
[Code]
Provable Guarantees for Decision Tree Induction: the Agnostic Setting (ICML 2020)
[Paper]
Decision Trees for Decision-Making under the Predict-then-Optimize Framework (ICML 2020)
[Paper]
The Tree Ensemble Layer: Differentiability meets Conditional Computation (ICML 2020)
[Code]
Generalized and Scalable Optimal Sparse Decision Trees (ICML 2020)
[Code]
Born-Again Tree Ensembles (ICML 2020)
[Code]
On Lp-norm Robustness of Ensemble Decision Stumps and Trees (ICML 2020)
[Paper]
Smaller, More Accurate Regression Forests Using Tree Alternating Optimization (ICML 2020)
[Paper]
Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)
[Paper]
Speeding up Very Fast Decision Tree with Low Computational Cost (IJCAI 2020)
[Paper]
PyDL8.5: a Library for Learning Optimal Decision Trees (IJCAI 2020)
[Code]
Geodesic Forests (KDD 2020)
[Paper]
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (NeurIPS 2020)
[Paper]
Estimating Decision Tree Learnability with Polylogarithmic Sample Complexity (NeurIPS 2020)
[Paper]
Universal Guarantees for Decision Tree Induction via a Higher-Order Splitting Criterion (NeurIPS 2020)
[Paper]
Smooth And Consistent Probabilistic Regression Trees (NeurIPS 2020)
[Paper]
An Efficient Adversarial Attack for Tree Ensembles (NeurIPS 2020)
[Code]
Decision Trees as Partitioning Machines to Characterize their Generalization Properties (NeurIPS 2020)
[Paper]
Evidence Weighted Tree Ensembles for Text Classification (SIGIR 2020)
[Paper]
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)
[Paper]
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (AAAI 2019)
[Paper]
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals (AAAI 2019)
[Paper]
Weighted Oblique Decision Trees (AAAI 2019)
[Paper]
Learning Optimal Classification Trees Using a Binary Linear Program Formulation (AAAI 2019)
[Paper]
Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees (AAAI 2019)
[Paper]
XBART: Accelerated Bayesian Additive Regression Trees (AISTATS 2019)
[Paper]
Interaction Detection with Bayesian Decision Tree Ensembles (AISTATS 2019)
[Paper]
Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)
[Paper]
Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)
[Paper]
Interpreting CNNs via Decision Trees (CVPR 2019)
[Paper]
EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees (ICDM 2019)
[Code]
Fair Adversarial Gradient Tree Boosting (ICDM 2019)
[Paper]
Functional Transparency for Structured Data: a Game-Theoretic Approach (ICML 2019)
[Paper]
Incorporating Grouping Information into Bayesian Decision Tree Ensembles (ICML 2019)
[Paper]
Adaptive Neural Trees (ICML 2019)
[Code]
Robust Decision Trees Against Adversarial Examples (ICML 2019)
[Code]
Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples (IJCAI 2019)
[Paper]
FAHT: An Adaptive Fairness-aware Decision Tree Classifier (IJCAI 2019)
[Code]
Inter-node Hellinger Distance based Decision Tree (IJCAI 2019)
[R Code]
Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)
[Code]
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees (IJCAI 2019)
[Paper]
Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (KDD 2019)
[Paper]
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers (NeurIPS 2019)
[Code]
Partitioning Structure Learning for Segmented Linear Regression Trees (NeurIPS 2019)
[Paper]
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)
[Code]
Optimal Decision Tree with Noisy Outcomes (NeurIPS 2019)
[Code]
Regularized Gradient Boosting (NeurIPS 2019)
[Paper]
Optimal Sparse Decision Trees (NeurIPS 2019)
[Code]
MonoForest framework for tree ensemble analysis (NeurIPS 2019)
[Code]
Calibrating Probability Estimation Trees using Venn-Abers Predictors (SDM 2019)
[Paper]
Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization (SDM 2019)
[Paper]
Forest Packing: Fast Parallel, Decision Forests (SDM 2019)
[Paper]
Block-distributed Gradient Boosted Trees (SIGIR 2019)
[Paper]
Entity Personalized Talent Search Models with Tree Interaction Features (WWW 2019)
[Paper]
MERCS: Multi-Directional Ensembles of Regression and Classification Trees (AAAI 2018)
[Code]
Differential Performance Debugging With Discriminant Regression Trees (AAAI 2018)
[Code]
Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction (AAAI 2018)
[Paper]
MDP-Based Cost Sensitive Classification Using Decision Trees (AAAI 2018)
[Paper]
Generative Adversarial Image Synthesis With Decision Tree Latent Controller (CVPR 2018)
[Code]
Enhancing Very Fast Decision Trees with Local Split-Time Predictions (ICDM 2018)
[Code]
Realization of Random Forest for Real-Time Evaluation through Tree Framing (ICDM 2018)
[Paper]
Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)
[Code]
Learning Optimal Decision Trees with SAT (IJCAI 2018)
[Paper]
Extremely Fast Decision Tree (KDD 2018)
[Code]
RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization (KDD 2018)
[Paper]
CatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)
[Code]
Active Learning for Non-Parametric Regression Using Purely Random Trees (NIPS 2018)
[Paper]
Alternating Optimization of Decision Trees with Application to Learning Sparse Oblique Trees (NIPS 2018)
[Paper]
Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)
[Code]
Transparent Tree Ensembles (SIGIR 2018)
[Paper]
Privacy-aware Ranking with Tree Ensembles on the Cloud (SIGIR 2018)
[Paper]
BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)
[Paper]
Latency Reduction via Decision Tree Based Query Construction (CIKM 2017)
[Paper]
Enumerating Distinct Decision Trees (ICML 2017)
[Paper]
Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)
[Code]
Consistent Feature Attribution for Tree Ensembles (ICML 2017)
[Code]
Extremely Fast Decision Tree Mining for Evolving Data Streams (KDD 2017)
[Paper]
CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)
[Code]
LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)
[Code]
Variable Importance Using Decision Trees (NIPS 2017)
[Paper]
A Unified Approach to Interpreting Model Predictions (NIPS 2017)
[Code]
Pruning Decision Trees via Max-Heap Projection (SDM 2017)
[Paper]
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees (UAI 2017)
[Paper]
Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility (UAI 2017)
[Paper]
GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)
[Paper]
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees (CVPR 2016)
[Paper]
Online Learning with Bayesian Classification Trees (CVPR 2016)
[Paper]
Accurate Robust and Efficient Error Estimation for Decision Trees (ICML 2016)
[Paper]
Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)
[Paper]
Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)
[Paper]
XGBoost: A Scalable Tree Boosting System (KDD 2016)
[Code]
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale (NIPS 2016)
[Paper]
A Communication-Efficient Parallel Algorithm for Decision Tree (NIPS 2016)
[Code]
Exploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles (SIGIR 2016)
[Code]
Post-Learning Optimization of Tree Ensembles for Efficient Ranking (SIGIR 2016)
[Paper]
DART: Dropouts Meet Multiple Additive Regression Trees (AISTATS 2015)
[Code]
Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-level Appearance Models (CVPR 2015)
[Paper]
Face Alignment Using Cascade Gaussian Process Regression Trees (CVPR 2015)
[Code]
Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)
[Paper]
Entropy Evaluation Based on Confidence Intervals of Frequency Estimates : Application to the Learning of Decision Trees (ICML 2015)
[Paper]
Large-scale Distributed Dependent Nonparametric Trees (ICML 2015)
[Paper]
Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)
[Paper]
A Decision Tree Framework for Spatiotemporal Sequence Prediction (KDD 2015)
[Paper]
Efficient Non-greedy Optimization of Decision Trees (NIPS 2015)
[Paper]
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees (SIGIR 2015)
[Paper]
On Building Decision Trees from Large-scale Data in Applications of On-line Advertising (CIKM 2014)
[Paper]
Fast Supervised Hashing with Decision Trees for High-Dimensional Data (CVPR 2014)
[Paper]
One Millisecond Face Alignment with an Ensemble of Regression Trees (CVPR 2014)
[Paper]
The return of AdaBoost.MH: multi-class Hamming trees (ICLR 2014)
[Paper]
Diagnosis Determination: Decision Trees Optimizing Simultaneously Worst and Expected Testing Cost (ICML 2014)
[Paper]
Learning Multiple-Question Decision Trees for Cold-Start Recommendation (WSDM 2013)
[Paper]
Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees (ICCV 2013)
[Paper]
Conformal Prediction Using Decision Trees (ICDM 2013)
[Paper]
Focal-Test-Based Spatial Decision Tree Learning: A Summary of Results (ICDM 2013)
[Paper]
Top-down Particle Filtering for Bayesian Decision Trees (ICML 2013)
[Paper]
Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)
[Paper]
Knowledge Compilation for Model Counting: Affine Decision Trees (IJCAI 2013)
[Paper]
Understanding Variable Importances in Forests of Randomized Trees (NIPS 2013)
[Paper]
Regression-tree Tuning in a Streaming Setting (NIPS 2013)
[Paper]
Learning Max-Margin Tree Predictors (UAI 2013)
[Paper]
ConfDTree: Improving Decision Trees Using Confidence Intervals (ICDM 2012)
[Paper]
Improved Information Gain Estimates for Decision Tree Induction (ICML 2012)
[Paper]
Learning Partially Observable Models Using Temporally Abstract Decision Trees (NIPS 2012)
[Paper]
Subtree Replacement in Decision Tree Simplification (SDM 2012)
[Paper]
Syntactic Decision Tree LMs: Random Selection or Intelligent Design (EMNLP 2011)
[Paper]
Decision Tree Fields (ICCV 2011)
[Paper]
Confidence in Predictions from Random Tree Ensembles (ICDM 2011)
[Paper]
Speeding-Up Hoeffding-Based Regression Trees With Options (ICML 2011)
[Paper]
Density Estimation Trees (KDD 2011)
[Paper]
Bagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)
[Paper]
On the Complexity of Decision Making in Possibilistic Decision Trees (UAI 2011)
[Paper]
Adaptive Bootstrapping of Recommender Systems Using Decision Trees (WSDM 2011)
[Paper]
Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)
[Paper]
Decision Trees for Uplift Modeling (ICDM 2010)
[Paper]
Learning Markov Network Structure with Decision Trees (ICDM 2010)
[Paper]
Multivariate Dyadic Regression Trees for Sparse Learning Problems (NIPS 2010)
[Paper]
Fast and Accurate Gene Prediction by Decision Tree Classification (SDM 2010)
[Paper]
A Robust Decision Tree Algorithm for Imbalanced Data Sets (SDM 2010)
[Paper]
Feature Selection for Ranking Using Boosted Trees (CIKM 2009)
[Paper]
Thai Word Segmentation with Hidden Markov Model and Decision Tree (PAKDD 2009)
[Paper]
Parameter Estimdation in Semi-Random Decision Tree Ensembling on Streaming Data (PAKDD 2009)
[Paper]
DTU: A Decision Tree for Uncertain Data (PAKDD 2009)
[Paper]
Bayes Optimal Classification for Decision Trees (ICML 2008)
[Paper]
A New Credit Scoring Method Based on Rough Sets and Decision Tree (PAKDD 2008)
[Paper]
A Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees (PAKDD 2008)
[Paper]
BOAI: Fast Alternating Decision Tree Induction Based on Bottom-Up Evaluation (PAKDD 2008)
[Paper]
A General Framework for Estimating Similarity of Datasets and Decision Trees: Exploring Semantic Similarity of Decision Trees (SDM 2008)
[Paper]
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data (SDM 2008)
[Paper]
Additive Groves of Regression Trees (ECML 2007)
[Paper]
Decision Tree Instability and Active Learning (ECML 2007)
[Paper]
Ensembles of Multi-Objective Decision Trees (ECML 2007)
[Paper]
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble (ECML 2007)
[Paper]
Sample Compression Bounds for Decision Trees (ICML 2007)
[Paper]
A Tighter Error Bound for Decision Tree Learning Using PAC Learnability (IJCAI 2007)
[Paper]
Keep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary (IJCAI 2007)
[Paper]
Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)
[Paper]
Scalable Look-ahead Linear Regression Trees (KDD 2007)
[Paper]
Mining Optimal Decision Trees from Itemset Lattices (KDD 2007)
[Paper]
A Hybrid Multi-group Privacy-Preserving Approach for Building Decision Trees (PAKDD 2007)
[Paper]
A Fast Decision Tree Learning Algorithm (AAAI 2006)
[Paper]
Anytime Induction of Decision Trees: An Iterative Improvement Approach (AAAI 2006)
[Paper]
When a Decision Tree Learner Has Plenty of Time (AAAI 2006)
[Paper]
Decision Trees for Functional Variables (ICDM 2006)
[Paper]
Cost-Sensitive Decision Tree Learning for Forensic Classification (ECML 2006)
[Paper]
Improving the Ranking Performance of Decision Trees (ECML 2006)
[Paper]
A General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees (KDD 2006)
[Paper]
Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction (PAKDD 2006)
[Paper]
Variable Randomness in Decision Tree Ensembles (PAKDD 2006)
[Paper]
Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees (PAKDD 2006)
[Paper]
Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics (PKDD 2006)
[Paper]
k-Anonymous Decision Tree Induction (PKDD 2006)
[Paper]
Use of Expert Knowledge for Decision Tree Pruning (AAAI 2005)
[Paper]
Model Selection in Omnivariate Decision Trees (ECML 2005)
[Paper]
Combining Bias and Variance Reduction Techniques for Regression Trees (ECML 2005)
[Paper]
Simple Test Strategies for Cost-Sensitive Decision Trees (ECML 2005)
[Paper]
Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches (ICDM 2005)
[Paper]
Exploiting Informative Priors for Bayesian Classification and Regression Trees (IJCAI 2005)
[Paper]
Ranking Cases with Decision Trees: a Geometric Method that Preserves Intelligibility (IJCAI 2005)
[Paper]
Maximizing Tree Diversity by Building Complete-Random Decision Trees (PAKDD 2005)
[Paper]
Hybrid Cost-Sensitive Decision Tree (PKDD 2005)
[Paper]
Tree2 - Decision Trees for Tree Structured Data (PKDD 2005)
[Paper]
Building Decision Trees on Records Linked through Key References (SDM 2005)
[Paper]
Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases (SDM 2005)
[Paper]
Boosted Decision Trees for Word Recognition in Handwritten Document Retrieval (SIGIR 2005)
[Paper]
Occam's Razor and a Non-Syntactic Measure of Decision Tree Complexity (AAAI 2004)
[Paper]
Using Emerging Patterns and Decision Trees in Rare-Class Classification (ICDM 2004)
[Paper]
Orthogonal Decision Trees (ICDM 2004)
[Paper]
Improving the Reliability of Decision Tree and Naive Bayes Learners (ICDM 2004)
[Paper]
Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data (ICDM 2004)
[Paper]
Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams (ICDM 2004)
[Paper]
Lookahead-based Algorithms for Anytime Induction of Decision Trees (ICML 2004)
[Paper]
Decision Trees with Minimal Costs (ICML 2004)
[Paper]
Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)
[Paper]
Detecting Structural Metadata with Decision Trees and Transformation-Based Learning (NAACL 2004)
[Paper]
On the Adaptive Properties of Decision Trees (NIPS 2004)
[Paper]
A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index (PAKDD 2004)
[Paper]
Ensembles of Cascading Trees (ICDM 2003)
[Paper]
Postprocessing Decision Trees to Extract Actionable Knowledge (ICDM 2003)
[Paper]
K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier (ICDM 2003)
[Paper]
Identifying Markov Blankets with Decision Tree Induction (ICDM 2003)
[Paper]
Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy (ICDM 2003)
[Paper]
Boosting Lazy Decision Trees (ICML 2003)
[Paper]
Decision Tree with Better Ranking (ICML 2003)
[Paper]
Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction (IJCAI 2003)
[Paper]
Efficient Decision Tree Construction on Streaming Data (KDD 2003)
[Paper]
PaintingClass: Interactive Construction Visualization and Exploration of Decision Trees (KDD 2003)
[Paper]
Accurate Decision Trees for Mining High-Speed Data Streams (KDD 2003)
[Paper]
Near-Minimax Optimal Classification with Dyadic Classification Trees (NIPS 2003)
[Paper]
Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule (PAKDD 2003)
[Paper]
Arbogodai: a New Approach for Decision Trees (PKDD 2003)
[Paper]
Communication and Memory Efficient Parallel Decision Tree Construction (SDM 2003)
[Paper]
Decision Tree Classification of Spatial Data Patterns from Videokeratography using Zernicke Polynomials (SDM 2003)
[Paper]
Heterogeneous Forests of Decision Trees (ICANN 2002)
[Paper]
Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)
[Paper]
Solving the Fragmentation Problem of Decision Trees by Discovering Boundary Emerging Patterns (ICDM 2002)
[Paper]
Learning Decision Trees Using the Area Under the ROC Curve (ICML 2002)
[Paper]
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction (ICML 2002)
[Paper]
Efficiently Mining Frequent Trees in a Forest (KDD 2002)
[Paper]
SECRET: a Scalable Linear Regression Tree Algorithm (KDD 2002)
[Paper]
Instability of Decision Tree Classification Algorithms (KDD 2002)
[Paper]
Extracting Decision Trees From Trained Neural Networks (KDD 2002)
[Paper]
Dyadic Classification Trees via Structural Risk Minimization (NIPS 2002)
[Paper]
Approximate Splitting for Ensembles of Trees using Histograms (SDM 2002)
[Paper]
Message Length as an Effective Ockham's Razor in Decision Tree Induction (AISTATS 2001)
[Paper]
SQL Database Primitives for Decision Tree Classifiers (CIKM 2001)
[Paper]
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees (ECML 2001)
[Paper]
Backpropagation in Decision Trees for Regression (ECML 2001)
[Paper]
Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction (ECML 2001)
[Paper]
Mining Decision Trees from Data Streams in a Mobile Environment (ICDM 2001)
[Paper]
Efficient Determination of Dynamic Split Points in a Decision Tree (ICDM 2001)
[Paper]
A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)
[Paper]
Efficient Algorithms for Decision Tree Cross-Validation (ICML 2001)
[Paper]
Bias Correction in Classification Tree Construction (ICML 2001)
[Paper]
Breeding Decision Trees Using Evolutionary Techniques (ICML 2001)
[Paper]
Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers (ICML 2001)
[Paper]
Temporal Decision Trees or the lazy ECU vindicated (IJCAI 2001)
[Paper]
Data Mining Criteria for Tree-based Regression and Classification (KDD 2001)
[Paper]
A Decision Tree of Bigrams is an Accurate Predictor of Word Sense (NAACL 2001)
[Paper]
Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron (PAKDD 2001)
[Paper]
A Scalable Algorithm for Rule Post-pruning of Large Decision Trees (PAKDD 2001)
[Paper]
Optimizing the Induction of Alternating Decision Trees (PAKDD 2001)
[Paper]
Interactive Construction of Decision Trees (PAKDD 2001)
[Paper]
Bloomy Decision Tree for Multi-objective Classification (PKDD 2001)
[Paper]
A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment (SDM 2001)
[Paper]
Tagging Unknown Proper Names Using Decision Trees (ACL 2000)
[Paper]
Clustering Through Decision Tree Construction (CIKM 2000)
[Paper]
Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling (ECML 2000)
[Paper]
Investigation and Reduction of Discretization Variance in Decision Tree Induction (ECML 2000)
[Paper]
Nonparametric Regularization of Decision Trees (ECML 2000)
[Paper]
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (ICML 2000)
[Paper]
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (ICML 2000)
[Paper]
Growing Decision Trees on Support-less Association Rules (KDD 2000)
[Paper]
Efficient Algorithms for Constructing Decision Trees with Constraints (KDD 2000)
[Paper]
Interactive Visualization in Mining Large Decision Trees (PAKDD 2000)
[Paper]
VQTree: Vector Quantization for Decision Tree Induction (PAKDD 2000)
[Paper]
Some Enhencements of Decision Tree Bagging (PKDD 2000)
[Paper]
Combining Multiple Models with Meta Decision Trees (PKDD 2000)
[Paper]
Induction of Multivariate Decision Trees by Using Dipolar Criteria (PKDD 2000)
[Paper]
Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms (PKDD 2000)
[Paper]
Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens (AISTATS 1999)
[Paper]
POS Tags and Decision Trees for Language Modeling (EMNLP 1999)
[Paper]
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)
[Paper]
The Alternating Decision Tree Learning Algorithm (ICML 1999)
[Code]
Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)
[Paper]
Using a Permutation Test for Attribute Selection in Decision Trees (ICML 1998)
[Paper]
A Fast and Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (ICML 1998)
[Paper]
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (ICML 1997)
[Paper]
Option Decision Trees with Majority Votes (ICML 1997)
[Paper]
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction (ICML 1997)
[Paper]
Functional Models for Regression Tree Leaves (ICML 1997)
[Paper]
The Effects of Training Set Size on Decision Tree Complexity (ICML 1997)
[Paper]
Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis (NIPS 1997)
[Paper]
Data-Dependent Structural Risk Minimization for Perceptron Decision Trees (NIPS 1997)
[Paper]
Generalization in Decision Trees and DNF: Does Size Matter (NIPS 1997)
[Paper]
Non-Linear Decision Trees - NDT (ICML 1996)
[Paper]
Learning Relational Concepts with Decision Trees (ICML 1996)
[Paper]
An Exact Probability Metric for Decision Tree Splitting (AISTATS 1995)
[Paper]
On Pruning and Averaging Decision Trees (ICML 1995)
[Paper]
On Handling Tree-Structured Attributed in Decision Tree Learning (ICML 1995)
[Paper]
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation (ICML 1995)
[Paper]
Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves (ICML 1995)
[Paper]
Efficient Algorithms for Finding Multi-way Splits for Decision Trees (ICML 1995)
[Paper]
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (ICML 1995)
[Paper]
Boosting Decision Trees (NIPS 1995)
[Paper]
Using Pairs of Data-Points to Define Splits for Decision Trees (NIPS 1995)
[Paper]
A New Pruning Method for Solving Decision Trees and Game Trees (UAI 1995)
[Paper]
In Defense of C4.5: Notes Learning One-Level Decision Trees (ICML 1994)
[Paper]
An Improved Algorithm for Incremental Induction of Decision Trees (ICML 1994)
[Paper]
Decision Tree Parsing using a Hidden Derivation Model (NAACL 1994)
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