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…
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48 results for “CART classifier”
Bayesian CART models improve insurance claims frequency prediction and interpretation.
problem Improving accuracy and interpretability in insurance pricing models.
method Introducing Bayesian CART models for claims frequency, implementing MCMC algorithm for posterior tree exploration, and using DIC for model selection.
result Bayesian CART models can better classify policy-holders into risk groups.
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.
Paper improves job scheduling by accurately predicting runtime classes.
problem Improving scheduling performance by separating short jobs from long jobs.
method Uses a CART classifier trained on Gaussian mixture representations of job runtimes.
result Overall accuracy of 90% for separating short jobs from long jobs.
Policy-Oriented Binary Classification: Improving (KD-)CART Final Splits for Subpopulation Targetingstat.ML
New split rules improve subpopulation targeting in policy-making.
problem Improving binary classification for subpopulation targeting in policy-making.
method MDFS, PFS, wEFS for maximizing distance and penalizing final splits.
result Proposed methods target more vulnerable subpopulations than classic CART/KD-CART.
This paper explains CART random forests using stochastic control theory.
problem Understanding the inner workings of CART random forests.
method Developed a stochastic-control perspective on CART random forests, interpreting feature subsampling as a random feasible action set and the split rule as a policy.
result Established that the CART policy is locally stabilizing but globally suboptimal for the forest objective.
This work improves mixing rates for Bayesian CART, a key component of BART.
problem Understanding and improving mixing rates for Bayesian inference with MCMC.
method Derived upper bounds on mixing times, provided sufficient conditions for polynomial mixing, and proposed Twiggy Bayesian CART.
result Twiggy Bayesian CART achieves polynomial mixing without assuming signal connectivity.
Covariance-Driven Regression Trees reduce overfitting in CART.
problem Overfitting in CART decision trees, especially with small sample sizes.
method Covariance-driven splitting criterion for regression trees (CovRT).
result CovRT achieves superior prediction accuracy compared to CART in simulations and real-world tasks.
Bregman perspective on CART provides a unified framework for impurity measures.
problem Unifying impurity measures in CART
method Bregman divergence approach
result Unified framework for impurity measures
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.
Graph-Valued Regressionstat.ML
Undirected graphical models encode in a graph the dependency structure of a random vector . In many applications, it is of interest to model given another random vector as input. We refer to the problem of estimating the graph of conditioned on as ``graph-valued regression.'' In this pap…
The paper analyzes the convergence of CART under a SID condition, improving previous results.
problem Investigating the convergence rate of CART under a sufficient impurity decrease condition.
method Established an upper bound on prediction error under SID condition, introduced easily verifiable conditions.
result Improved convergence rate of CART under SID condition, demonstrated examples of error bound limitations.
Sparse learning with CARTstat.ML
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.
Analyzing CARTstat.ML
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.
Study uses AI techniques to predict bank customer solvency.
problem Predicting the solvency of bank customers.
method Data preprocessing, CART decision tree method, SPSS tool.
result Model accuracy and precision of 71%, error rate of 29%.
The paper develops a cross-validation method for improving signal denoising techniques.
problem Improving signal denoising methods for nonparametric regression.
method Develops a general cross-validation framework for signal denoising and applies it to Trend Filtering and Dyadic CART.
result Cross validated versions of Trend Filtering and Dyadic CART achieve nearly optimal convergence rates.
Estimates piecewise polynomials and bounded variation functions using optimal decision trees.
problem Estimating piecewise smooth functions in general dimensions.
method Dyadic CART and Optimal Regression Tree (ORT) estimators for piecewise polynomials and bounded variation functions.
result Oracle inequalities and risk bounds for ORT estimators, demonstrating adaptivity and optimality.
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.
Designing effective and efficient classifier for pattern analysis is a key problem in machine learning and computer vision. Many the solutions to the problem require to perform logic operations such as `and', `or', and `not'. Classification and regression tree (CART) include these operations explicitly. Other methods s…
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…
Slow-Growing Treesstat.ML
A single slow-growing tree matches Random Forest's performance.
problem Matching Random Forest's performance with a single tree.
method SGT uses a learning rate to tame CART's greedy algorithm, improving on greedy ML algorithms.
result SGT and tree ensembles like Booging, BT, and RF improve performance.
Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Treesstat.ML
Tree-structured boosting connects gradient boosted stumps and full decision trees.
problem Connecting gradient boosted stumps and full decision trees.
method Introducing tree-structured boosting to create a single decision tree.
result Tree-structured boosting produces models equivalent to CART or gradient boosted stumps at the extremes.
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
Approach scales regression for large datasets using CART segmentation.
problem Scaling regression to large datasets in various applications.
method Two-step approach: CART segmentation followed by model development for each segment.
result Predictive performance matches Gradient Boosted Trees.
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground cover. The aim is to derive a supervised learning model for the classification usin…
Paper detects bias in AI medical models using CART.
problem Ensuring fairness in AI medical decision support systems.
method Uses Classification and Regression Trees (CART) algorithm to identify bias.
result Validated the CART approach in both synthetic and real-world data.
Paper explores grafting consistent estimators to improve Random Forest consistency.
problem Ensuring Random Forests are consistent despite their performance.
method Grafting consistent estimators onto a shallow CART.
result Grafted estimators provide a consistency guarantee and perform well empirically.
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole genetic material of living organisms is an example of such a case, where the number of feature exceeds 10^7. Three human pathogens were used to…
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
This study uses machine learning to predict sovereign credit ratings and identifies key factors.
problem Predicting sovereign credit ratings and identifying important factors.
method Used Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naïve Bayes (NB), and Ordered Logit (OL) models.
result MLP is the best model for predicting sovereign credit ratings with a 68% accuracy.
This study examines hyperparameter tuning for CART and C4.5 DT algorithms.
problem Finding optimal hyperparameters for DT algorithms to improve predictive performance.
method Comprehensive empirical study with 94 datasets, using IRACE for tuning.
result Different HP profiles provide significant improvements for CART, but less for C4.5.
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.
Previous algorithms for constructing regression tree models for longitudinal and multiresponse data have mostly followed the CART approach. Consequently, they inherit the same selection biases and computational difficulties as CART. We propose an alternative, based on the GUIDE approach, that treats each longitudinal d…
Machine learning outperforms traditional models in wind power forecasting.
problem Improving wind power prediction accuracy using machine learning.
method Used CART-Bagging algorithm and refined predictor selection methods.
result Machine learning algorithms, especially CART-Bagging, provide stable and promising wind power forecasts.
The paper uses Column Generation for faster construction of binary decision trees.
problem Constructing efficient univariate binary decision trees for classification tasks.
method Proposes an Integer Linear Programming (ILP) formulation and solves it via Column Generation based heuristic.
result The approach is competitive with state-of-the-art ILP-based algorithms and can handle large datasets.
Approach uses neural networks to learn and extrapolate equations from data.
problem Learning concise equations from data for extrapolation and control.
method Shallow neural network approach to identify functional relations.
result Can extrapolate to unseen domains and learn true underlying equations.
GapTV improves interpretability in small feature spaces.
problem Estimating regression functions with small feature sets and high interpretability needs.
method Divides feature space into blocks, fits values jointly using convex optimization, incorporates automatic hyperparameter tuning.
result GapTV finds a better balance between accuracy and interpretability compared to CART and CRISP.
Kauri is a novel unsupervised binary tree for clustering that outperforms existing methods.
problem Learning a tree end-to-end for clustering without labels is an open challenge.
method Greedy maximization of the kernel KMeans objective without centroids.
result Kauri often outperforms existing unsupervised clustering methods, especially with non-linear kernels.
Recursive partitioning approaches producing tree-like models are a long standing staple of predictive modeling, in the last decade mostly as ``sub-learners'' within state of the art ensemble methods like Boosting and Random Forest. However, a fundamental flaw in the partitioning (or splitting) rule of commonly used tre…
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.
WRAAC uses Wasserstein distance for robust reinforcement learning.
problem Lack of quantified robustness to system dynamics in existing reinforcement learning algorithms.
method Leverages Wasserstein distance to connect state disturbance to transition kernel disturbance, reducing infinite-dimensional optimization to a finite-dimensional problem.
result Designs a novel algorithm, WRAAC, that achieves robust reinforcement learning.
Paper predicts daytime hypoglycemia using CGM data and CART.
problem Accurately predicting daytime hypoglycemia to prevent dangerous situations.
method Classification and Regression Tree (CART) applied to CGM data.
result Model detects almost 80% of hypoglycemic events 15 min in advance.
MVTV improves interpretability in low-dimensional regression.
problem Estimating regression functions with few features and high interpretability needs.
method MVTV divides space into blocks, fits values jointly, and optimizes automatically.
result MVTV outperforms CART and CRISP in both complexity and human interpretability studies.
A new method improves simple models by transferring information from deep networks.
problem Improving simple models with limited resources and interpretability.
method ProfWeight method using confidence scores from deep neural network intermediate layers to train simpler models.
result Significant improvement in test accuracy of simple models (up to 13%) on a real manufacturing problem.
New methods improve variance estimation in random forests using IJ.
problem Estimating prediction variance in random forests.
method Simulation experiments with different resampling and base learner methods.
result CI trees and subsampling lead to more accurate variance estimation.
This review chronicles AI algorithms for cervical cancer screening.
problem Automated screening of cervical cancer using AI methods.
method Analysis of various machine learning algorithms and clustering techniques.
result Holistic review of computational methods over time.
Model selection is a crucial issue in machine-learning and a wide variety of penalisation methods (with possibly data dependent complexity penalties) have recently been introduced for this purpose. However their empirical performance is generally not well documented in the literature. It is the goal of this paper to in…
Max-Cut decision tree improves classification accuracy and reduces computation time.
problem Improving decision tree accuracy and efficiency for complex classification tasks.
method Alternative splitting metric (max cut) and PCA-based feature selection at each node.
result 49% improvement in accuracy with 94% reduction in CPU time on CIFAR-100 data.