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Bootstrap aggregating

Known as: Bootstrap aggregation, Bootstrapped Aggregation, Bootstrapping (machine learning) 
Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine… 
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Related topics

Related topics

22 relations
AdaBoostBias–variance tradeoffBoosting (machine learning)Bootstrapping (statistics)

Broader (1)

Computational statistics

Papers overview

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2018
2018
Regularized Extreme Learning Machine Ensemble Using Bagging for Tropical Cyclone Tracks Prediction
This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) and its… 
2017
2017
Feature Ranking for Multi-target Regression with Tree Ensemble Methods
In this work, we address the task of feature ranking for multi-target regression (MTR). The task of MTR concerns problems where… 
2016
2016
Ensemble Tree Learning Techniques for Magnetic Resonance Image Analysis
This paper shows a comparative study of boosting and bagging algorithms for magnetic resonance image (MRI) analysis and… 
2015
2015
Pruning Bagging Ensembles with Metalearning
Ensemble learning algorithms often benefit from pruning strategies that allow to reduce the number of individuals models and… 
2009
2009
HETEROGENEOUS ENSEMBLE CLASSIFICATION
The problem of multi-class classication is explored using heterogeneous ensemble classiers. Heterogeneous ensembles classiers are… 
2009
2009
Applied Taxonomy Techniques Intended for Strenuous Random Forest Robustness
Globalization and economic trade has change the scrutiny of facts from data to knowledge. For the same purpose data mining… 
2009
2009
An efficient classifier ensemble using SVM
Recently ensemble classification has attracted serious attention of machine learning community as a solution for improving… 
2004
2004
Combining classifiers for harmful document filtering
In this paper, we describe the experiments that we have carried out during the European Research Project NetProtect II that aims… 
2003
2003
Joint estimation of thresholds in a bi-threshold verification problem
Verification problems are usually posted as a 2-class problem and the objective is to verify if an observation belongs to a class… 
1995
1995
A Comparison of Methods for Learning and Combining Evidence From Multiple Models
Most previous work on multiple models has been done on a few domains. We present a com-parsion of three ways of learning multiple…