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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

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Removing Bias from Diverse Data Clusters for Ensemble Classification
Diversity plays an important role in successful ensemble classification. One way to diversify the base-classifiers in an ensemble… 
2010
2010
Hyperspectral data classification via sparse representation in homotopy
Sparse representation has significant success in many fields such as signal compression and reconstruction but to the best of our… 
2010
2010
Full-scale On-farm Pretreatment of Perennial Grasses with Dilute Acid for Fuel Ethanol Production
Biorefineries that rely on lignocellulosic feedstocks require dependable and safe methods for storing biomass. Storing biomass… 
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… 
Highly Cited
2009
Highly Cited
2009
Reducing Semantic Drift with Bagging and Distributional Similarity
Iterative bootstrapping algorithms are typically compared using a single set of hand-picked seeds. However, we demonstrate that… 
2009
2009
HETEROGENEOUS ENSEMBLE CLASSIFICATION
The problem of multi-class classication is explored using heterogeneous ensemble classiers. Heterogeneous ensembles classiers are… 
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
Texture classification of logged forests in tropical Africa using machine-learning algorithms
This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat… 
2000
2000
Bagging and Boosting a Treebank Parser
Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these… 
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… 
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