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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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2019
2019
Bagging and Boosting Ensembles for Conflict Resolution on Heterogeneous Data
In the era of big data and with the advent of the Internet of things (IoT) more and more of devices are being connected to… 
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
Using Ensemble Methods to Improve the Performance of Prediction - Statistical Analysis of a Myocardial Infarction Data Set from the HUNT Study
The main focus of this thesis is to evaluate the use of ensemble methods to improve the performance of prediction, when applied… 
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… 
2013
2013
RB: A NEW METHOD FOR CONSTRUCTING MULTI-LABEL CLASSIFIERS BASED ON RANDOM SELECTION AND BAGGING
In many real world prediction problems, a classifier must, or should, assign more than one label to an instance, e.g. prediction… 
2009
2009
HETEROGENEOUS ENSEMBLE CLASSIFICATION
The problem of multi-class classication is explored using heterogeneous ensemble classiers. Heterogeneous ensembles classiers are… 
2006
2006
Ensembles of Grafted Trees
Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method… 
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…