Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

Related topics

Related topics

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

Broader (1)

Computational statistics

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
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… 
2014
2014
Ensemble Neural Network and K-NN Classifiers for Intrusion Detection
In this paper we present the ensemble algorithm to improve the intrusion detection precision. Ensemble classifier is a technique… 
2010
2010
Algorithm of Partition based Network Boosting for imbalanced data classification
Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the… 
2009
2009
HETEROGENEOUS ENSEMBLE CLASSIFICATION
The problem of multi-class classication is explored using heterogeneous ensemble classiers. Heterogeneous ensembles classiers are… 
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… 
2001
2001
Bagging classifiers based on Kernel density estimators
A lot of research is being conducted on combining classification rules (classifiers) to produce a single one, known as an… 
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…