Boosted Classification Trees and Class Probability/Quantile Estimation

No abstract is available for this paper.

• Publication year

  2007

• Venue

  Journal of machine learning research

• Publication date

  2007-12-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.5555/1314498.1314514
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Evidence Contrary to the Statistical View of Boosting

  2008cited by this paper
• Some Theory for Generalized Boosting Algorithms

  2006cited by this paper
• Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications

  2005influential reference
• Boosting with early stopping: Convergence and consistency

  2005cited by this paper
• Boosting as a Regularized Path to a Maximum Margin Classifier

  2004cited by this paper
• A Multiple Resampling Method for Learning from Imbalanced Data Sets

  2004cited by this paper
• Expression Data

  2004influential reference
• On the Rate of Convergence of Regularized Boosting Classifiers

  2003cited by this paper
• Boosting for Tumor Classification with Gene Expression Data

  2003influential reference
• Process consistency for AdaBoost

  2003cited by this paper
• SMOTEBoost: Improving Prediction of the Minority Class in Boosting

  2003cited by this paper
• On the Bayes-risk consistency of regularized boosting methods

  2003cited by this paper
• The Elements of Statistical Learning

  2003influential reference
• SMOTE: Synthetic Minority Over-sampling Technique

  2002cited by this paper
• Boosting to correct inductive bias in text classification

  2002cited by this paper
• Boosting and Maximum Likelihood for Exponential Models

  2001cited by this paper
• Evaluating boosting algorithms to classify rare classes: comparison and improvements

  2001cited by this paper
• The Foundations of Cost-Sensitive Learning

  2001influential reference
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers

  2001cited by this paper
• Logistic Regression, AdaBoost and Bregman Distances

  2000cited by this paper
• Special Invited Paper-Additive logistic regression: A statistical view of boosting

  2000influential reference
• A Comparative Study of Cost-Sensitive Boosting Algorithms

  2000cited by this paper
• Does Boosting Over t: Views From an Exact Solution

  2000cited by this paper
• AdaCost: Misclassification Cost-Sensitive Boosting

  1999cited by this paper
• A simple, fast, and effective rule learner

  1999cited by this paper
• Potential Boosters?

  1999cited by this paper
• Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection

  1998cited by this paper
• Experiments with a New Boosting Algorithm

  1996influential reference
• Elicitation of Personal Probabilities and Expectations

  1971cited by this paper

• Solving multi-class imbalance problem with error-correcting codes

  2026cites this paper
• An Integrated Model of Natural Language Processing and Machine Learning (INM) for Autism Detection and classification from Random Symptom

  2025cites this paper
• Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review

  2025cites this paper
• Hybrid Machine learning and temporal-spatial fusion decision for real-time monitoring of drilling stage in ultrafast laser drilling

  2025cites this paper
• Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion

  2025cites this paper
• Estimating Calibrated Risks Using Focal Loss and Gradient-Boosted Trees for Clinical Risk Prediction

  2025cites this paper
• Rethinking the DeepSMOTE Penalty Term and its Role in Imbalanced Learning

  2025cites this paper
• Hybrid CNN and random forest model with late fusion for detection of autism spectrum disorder in Toddlers

  2025cites this paper
• A Cross-Condition Fault Disentangling Matching Network for Industrial Early Fault Enhancement

  2025cites this paper
• A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

  2024cites this paper
• A review of machine learning for modeling air quality: Overlooked but important issues

  2024cites this paper
• Relabeling & raking algorithm for imbalanced classification

  2024cites this paper
• Machine-learning assisted analysis on the seismic performance of steel reinforced concrete composite columns

  2024cites this paper
• Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for Imbalanced Data

  2024cites this paper
• Adaptive boosting with dynamic weight adjustment

  2024cites this paper
• Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration

  2024cites this paper
• Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance

  2024cites this paper
• An Industrial Fault Sample Reconstruction and Generation Method Under Limited Samples With Missing Information

  2024cites this paper
• Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement

  2024cites this paper
• PFSC: Parameter-free sphere classifier for imbalanced data classification

  2024cites this paper
• Multiple optimized ensemble learning for high-dimensional imbalanced credit scoring datasets

  2024cites this paper
• Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review.

  2023cites this paper
• Calibrating machine learning approaches for probability estimation: A comprehensive comparison

  2023cites this paper
• Online Learning From Incomplete and Imbalanced Data Streams

  2023cites this paper
• HSNF: Hybrid sampling with two-step noise filtering for imbalanced data classification

  2023cites this paper
• Appraising Machine and Deep Learning Techniques for Traffic Conflict Prediction with Class Imbalance

  2023cites this paper
• SFCWGAN-BiTCN with Sequential Features for Malware Detection

  2023cites this paper
• A Multi-level Feature Extraction Method for Heterogeneous Sample Enhancement

  2023cites this paper
• TOMBoost: a topic modeling based boosting approach for learning with class imbalance

  2022cites this paper
• Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

  2022influential citation
• GUM: A Guided Undersampling Method to Preprocess Imbalanced Datasets for Classification

  2022cites this paper
• Research on Improved Random Forest Algorithm for Highly Unbalanced Data

  2022cites this paper
• TDDA-Net: A transitive distant domain adaptation network for industrial sample enhancement

  2022cites this paper
• A Hybrid Approach for Binary Classification of Imbalanced Data

  2022cites this paper
• Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets

  2022cites this paper
• An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique

  2022cites this paper
• Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear Programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

  2022cites this paper
• Optimal Transport based Data Augmentation for Heart Disease Diagnosis and Prediction

  2022cites this paper
• On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning

  2022cites this paper
• Long-tailed visual recognition with deep models: A methodological survey and evaluation

  2022cites this paper
• GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

  2022cites this paper
• Sampling-based Software Prone Technique for an Optimal Prediction of Software Faults

  2022cites this paper
• A Modified Ant Lion Optimization Method and Its Application for Instance Reduction Problem in Balanced and Imbalanced Data

  2022cites this paper
• PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets

  2022cites this paper
• An Empirical And Comparatively Research On Under-Sampling & Over- Sampling Defect-Prone Data-Sets Model In Light Of Machine Learning

  2021cites this paper
• Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis

  2021cites this paper
• A Modified Borderline Smote with Noise Reduction in Imbalanced Datasets

  2021cites this paper
• Identification of Autism Spectrum Disorder using Deep Neural Network

  2021cites this paper
• RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

  2021cites this paper
• SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem

  2021cites this paper
• Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data

  2021cites this paper
• Handling Imbalanced Data for Real-Time Crash Prediction: Application of Boosting and Sampling Techniques

  2021cites this paper
• A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning

  2021cites this paper
• Incremental Weighted Ensemble Broad Learning System for Imbalanced Data

  2021cites this paper
• Effect of COVID-19 on Autism Spectrum Disorder: Prognosis, Diagnosis, and Therapeutics Based on AI

  2021cites this paper
• Transfer learning for spatio-temporal transferability of real-time crash prediction models.

  2021cites this paper
• Contingency‐based adaptive synthetic data generation to improve transient stability prediction in power systems

  2021cites this paper
• A reliable ensemble based approach to semi-supervised learning

  2021cites this paper
• Compensatory model for quantile estimation and application to VaR

  2021cites this paper
• A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning

  2021cites this paper
• Algorithmic approach to forecasting rare violent events

  2020cites this paper
• Industrial text analytics for reliability with derivative-free optimization

  2020cites this paper
• Learning a graph-based classifier for fault localization

  2020cites this paper
• Boosting Minority Class Prediction on Imbalanced Point Cloud Data

  2020cites this paper
• Better Boosting with Bandits for Online Learning

  2020cites this paper
• Data Analytics for Solar Energy in Promoting Smart Cities

  2020cites this paper
• Boosting methods for multi-class imbalanced data classification: an experimental review

  2020cites this paper
• Addressing class imbalance for logistic regression

  2020cites this paper
• Research Review on Imbalanced Learning Models in Embedded Intelligent Computing

  2020cites this paper
• Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

  2020cites this paper
• GNN-based Graph Anomaly Detection with Graph Anomaly Loss

  2020cites this paper
• Corporate distress prediction using random forest and tree net for india

  2020cites this paper
• Error-Bounded Graph Anomaly Loss for GNNs

  2020cites this paper
• Hybrid Classifier Ensemble for Imbalanced Data

  2020cites this paper
• Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice

  2020cites this paper
• ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification

  2020cites this paper
• An Experimental Evaluation of Imbalanced Learning and Time-Series Validation in the Context of CI/CD Prediction

  2020cites this paper
• Pedestrian detection using a moving camera: A novel framework for foreground detection

  2020cites this paper
• A Novel Model for Imbalanced Data Classification

  2020cites this paper
• Aided Selection of Sampling Methods for Imbalanced Data Classification

  2020cites this paper
• Prediction of autism spectrum disorder based on imbalanced resting-state fMRI data using clustering oversampling

  2019cites this paper
• Classification Problem in Imbalanced Datasets

  2019cites this paper
• A random forests quantile classifier for class imbalanced data

  2019cites this paper
• Feature augmentation for imbalanced classification with conditional mixture WGANs

  2019cites this paper
• Evolutionary-Based Weighted Broad Learning System for Imbalanced Learning

  2019cites this paper
• A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty

  2019cites this paper
• Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method

  2019cites this paper
• Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders

  2019cites this paper
• Almost politically acceptable criminal justice risk assessment

  2019cites this paper
• Neural Network-Based Undersampling Techniques

  2019cites this paper
• A new rule-based knowledge extraction approach for imbalanced datasets

  2019cites this paper
• Functional gradient approach to probabilistic minimax active learning

  2019cites this paper
• An Algorithmic Approach to Forecasting Rare Violent Events: An Illustration Based in IPV Perpetration

  2019cites this paper
• CrossCount: A Deep Learning System for Device-Free Human Counting Using WiFi

  2019cites this paper
• Survey on deep learning with class imbalance

  2019cites this paper
• Hybrid of Intelligent Minority Oversampling and PSO-Based Intelligent Majority Undersampling for Learning from Imbalanced Datasets

  2018cites this paper
• Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease

  2018cites this paper
• Calibrating AdaBoost for phoneme classification

  2018cites this paper
• VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals

  2018cites this paper
• A novel topic modeling based weighting framework for class imbalance learning

  2018cites this paper