Neural regression trees convert regression to classification more effectively.
problem Suboptimal approaches for regression via classification.
method Joint optimization framework for learning optimal discretization thresholds and feature selection in a neural regression tree.
result Empirically validated as state-of-the-art on challenging regression tasks.
A new Bayesian Decision Tree algorithm for regression and classification.
problem Costly construction of Bayesian Decision Trees.
method General Bayesian Decision Tree algorithm without Markov Chain Monte Carlo or pruning.
result Greedy-modal tree explains most of the information in numerical examples.
Stochastic Gradient Trees learn decision trees incrementally.
problem Learning decision trees using stochastic gradient information.
method Incremental learning setting, soft splits not used, new tree not constructed per update.
result Performs similarly to standard incremental classification trees, outperforms state of the art incremental regression trees, comparable to batch multi-instance learning methods.
Tree ensembles like RF and GBT can be seen as kernels, improving regression and classification performance.
problem Improving kernel methods for tree ensemble based models.
method Investigation of RF and GBT kernels in simulation and real data.
result RF and GBT kernels are competitive to their respective ensembles in higher dimensions, particularly with noisy features.
Two new methods improve monotonic constraint enforcement in regression and classification trees.
problem Improving monotonic constraints in regression and classification trees.
method Proposed two new methods: one yields better results than LightGBM, the other yields even better results but is slower.
result The best method consistently beats the current implementation of LightGBM, achieving up to 1% loss reduction.
Tree-based algorithm for functional data analysis reduces generalization error.
problem Classification and regression problems with functional data.
method Constrained convex optimization for weighted functional L2 space, multiple splitting rules, and weighted integral features. result Reduces generalization error while maintaining interpretability.
Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a…
TAO outperforms other decision tree algorithms in accuracy.
problem Optimizing decision tree algorithms for better performance.
method Comparison of TAO with established algorithms on various datasets.
result TAO achieves higher accuracy in nearly all datasets.
Decision trees are consistent for regression and classification tasks even with many predictors.
problem Consistency of decision trees with many predictors.
method CART and C4.5 methodology, oracle inequality, sparsity constraints.
result Decision trees and random forests are consistent for various types of data.
Online BSP-Forest improves space partitioning for large-scale classification and regression.
problem Efficient space partitioning for large-scale classification and regression problems.
method Developed an online BSP-Forest framework that expands space coverage and refines partition structure in real-time.
result Guaranteed universal consistency for both classification and regression problems.
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as an infinite mixture Dirichlet process. We show that B…
Proposes a deep tree-ensemble model for multi-output prediction.
problem Lack of efficient solutions for multi-output prediction.
method Integrates tree-embeddings into deep tree-ensembles for structured output prediction.
result Superior performance in multi-label classification and multi-target regression tasks.
A novel gradient-based method optimizes decision trees for complex tasks.
problem Training decision trees with arbitrary differentiable loss functions.
method Gradient-based optimization using first and second derivatives of loss functions.
result Improves accuracy and flexibility in decision tree optimization.
The paper studies bias and adaptivity of CART regression trees.
problem Bias and adaptivity of CART regression trees.
method Derives an interesting connection between bias and MDI measure of variable importance.
result Decision trees with CART have small bias and are adaptive to signal strength and direction.
New method boosts multiclass classification speed and accuracy.
problem Multiclass classification problem
method Factorized MultiClass Boosting (decomposing into regression tasks, solving with CART trees)
result Significantly faster and same quality as state-of-the-art solutions
Ensembles of classification and regression trees remain popular machine learning methods because they define flexible non-parametric models that predict well and are computationally efficient both during training and testing. During induction of decision trees one aims to find predicates that are maximally informative …
The paper proposes a method to improve random forest classification accuracy by weighting trees based on their decision path reliability.
problem Random forests' uniform voting fails to correct errors in regions where incorrect tree representations outnumber correct ones.
method The paper introduces using the structural pattern of each tree's decision path as an instance-adaptive reliability signal to identify and weight more reliable trees.
result Using the proposed method yields a statistically significant accuracy improvement over RF on 36 binary classification benchmarks.
Gradient boosting adapted for multi-label and multi-output tasks.
problem Joint prediction of multiple classification or regression outputs.
method Gradient tree boosting with random output projections.
result Random projection improves adaptation to different output correlation patterns.
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. T…
This work tackles manifold regression onto hyperbolic space for tree classification and taxonomy extension.
problem Performing manifold-valued regression onto an hyperbolic space for tree classification and taxonomy extension.
method Formulated as a manifold regression task in hyperbolic space, proposed a parametric deep learning model and a non-parametric kernel method.
result Hyperbolic-based estimators significantly outperform Euclidean space methods in taxonomy expansion.
BART and MOTR-BART improve tree-based predictions with local linear models.
problem Non-linearity and high-order interactions in data.
method Bayesian Additive Regression Trees (BART) and Model Trees BART (MOTR-BART) using piecewise linear functions.
result MOTR-BART achieves equal or better performance with fewer trees than BART.
A new type of distributional regression tree uses soft split rules for better predictive performance.
problem Estimating complete conditional distributions in regression.
method Distributional adaptive soft regression trees using multivariate soft split rules.
result The method outperforms various benchmark methods, especially in complex non-linear interactions.
A new randomized tree classifier outperforms traditional CARTs.
problem Traditional greedy CARTs are slow and inaccurate.
method Optimal decision trees using continuous optimization.
result Randomized tree classifier outperforms traditional CARTs.
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y given another random vector X as input. We refer to the problem of estimating the graph G(x) of Y conditioned on X=x as ``graph-valued regression.'' In this pap…
AMF is an online random forest algorithm for regression tasks.
problem Online learning for regression tasks with limited data.
method Mondrian Forests combined with Context Tree Weighting for efficient aggregation.
result AMF achieves competitive performance with optimal pruning, suitable for unknown function regularity.
A Bayesian approach to multilabel classification using tree-based models.
problem Challenges in multilabel classification due to complex label relationships and correlations.
method Bayesian Additive Regression Trees (BART) framework for modeling multilabel classification.
result Improved predictive performance compared to other models, including an oracle model.
CART can bias propensity score estimates with missing data, but multiple imputation is better.
problem Bias in propensity score estimation with CART and missing data.
method Examined CART performance with different approaches to missing data: direct CART, complete case analysis, and multiple imputation.
result Multiple imputation followed by CART outperformed direct CART with missing data.
pGMM kernel outperforms ordinary ridge regression and RBF kernel ridge regression without tuning.
problem Comparing pGMM kernel regression with other ridge regression methods.
method Implemented and compared pGMM kernel regression with ordinary ridge regression and RBF kernel ridge regression.
result pGMM kernel performs well without tuning and can match boosted trees with parameter tuning.
The paper studies statistical properties of CART regression trees.
problem Understanding the statistical properties of CART regression trees.
method The paper constructs a prior distribution on split points and solves a nonlinear optimization problem to bound the Pearson correlation between the optimal decision stump and response data.
result CART with cost-complexity pruning achieves an optimal complexity/goodness-of-fit tradeoff when the depth scales with the logarithm of the sample size.
Energy trees handle complex data structures with multiple variable types.
problem Handling intricate data structures with various types of covariates.
method Energy trees, a regression and classification model, use energy statistics to accommodate structured covariates of different types.
result Energy trees maintain statistical foundations, interpretability, and robustness to overfitting.
Infinite BART model selects number of trees and allows different functions for clusters.
problem Regression and classification analysis with automatic tree selection and cluster-specific functions.
method Incorporates an Indian Buffet process prior to select a subset of decision trees for each observation.
result Infinite BART model outperforms classic BART on simulated and real datasets.
Probability calibration trees improve accuracy of probability estimates.
problem Improving accuracy and calibration of probability estimates from classifiers.
method Probability calibration trees modify logistic model trees to learn different models in regions of the input space.
result Probability calibration trees outperform isotonic regression and Platt scaling in terms of root mean squared error.
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…
Improved efficient learning of neighbor representations for large datasets.
problem Efficiently learn neighbor representations for large datasets.
method Differentiable Boundary Sets algorithm that overcomes computational issues and improves accuracy.
result Significant reduction in training time and improved classification accuracy.
We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives…
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
problem Understanding the effectiveness of tree-based methods in finite-sample settings, especially symbolic feature selection.
method Local ranking perspective, finite-sample analysis, oracle bounds, posterior contraction results, concordant divergence statistics.
result New insights and statistics for evaluating symbolic feature mappings.
ABRF uses attention weights to improve RF performance.
problem Improving the performance of random forest models.
method Attention mechanism applied to random forest with optimization and gradient-based methods.
result The proposed ABRF models outperform standard RF models on various datasets.
New algorithm detects and handles concept drift in data streams.
problem Handling concept drift in data streams for timely predictions.
method Hybrid Forest algorithm combining Hoeffding Trees and fast startup.
result The algorithm outperforms other methods in classification and regression tasks.
Selective inference framework for CART trees to control error rates and coverage.
problem Inference on CART trees does not control Type 1 error rates and coverage.
method Selective inference framework conditioning on tree estimation, efficient algorithms.
result Proposes tests and intervals for CART trees with selective error control.
A hybrid model improves bankcard response classification accuracy.
problem Bankcard response classification accuracy improvement.
method CHAID for interaction detection in logistic regression.
result The hybrid model outperforms logistic regression in classification accuracy.
Paper studies ensemble probabilistic regression trees for smooth approximations.
problem Smooth approximations of regression functions.
method Ensemble versions of probabilistic regression trees.
result Ensemble probabilistic regression trees are consistent and perform well.
DiPriMe forests use private medians to create balanced tree splits for privacy-protected data.
problem Privacy concerns in training random forests due to multiple data queries.
method Proposes DiPriMe forests, which use a private median to generate balanced splits, ensuring differential privacy.
result DiPriMe forests achieve high utility while maintaining differential privacy, as shown both theoretically and empirically.
BART is extended to handle various response variables.
problem Modeling nonlinear regression functions for diverse response types.
method Generalized Bayesian Additive Regression Trees (GBART) for exponential family distributions.
result The posterior concentrates at a minimax rate for certain response distributions.
New loss function improves classification accuracy.
problem Negative expected accuracy as an alternative to cross entropy for classification.
method Empirical investigation using modified leaky version of expected accuracy.
result Comparable or better accuracy compared to cross entropy, robust to label noise.
Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penal…
This research compares gradient and Newton boosting methods in classification and regression.
problem The distinction between gradient descent and Newton updates in boosting algorithms is not well understood.
method Presented a unified framework for gradient and Newton boosting, and compared them with tree base learners.
result Newton boosting outperforms gradient and hybrid boosting in predictive accuracy on most datasets.