Comparing Pure Parallel Ensemble Creation Techniques Against Bagging

Authors:
Lawrence O. Hall;Kevin W. Bowyer;Robert E. Banfield;Divya Bhadoria;W. Philip Kegelmeyer;Steven Eschrich
Affiliations:
-;-;-;-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

Citing 10
Cited 9

The Strength of Weak Learnability

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Random Forests

Machine Learning
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Distributed learning with bagging-like performance

Pattern Recognition Letters
Logistic Regression, AdaBoost and Bregman Distances

COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory

A Comparison of Decision Tree Ensemble Creation Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence
Immune network based ensembles

Neurocomputing
Nonlinear Boosting Projections for Ensemble Construction

The Journal of Machine Learning Research
Boosting random subspace method

Neural Networks
Constructing ensembles of classifiers by means of weighted instance selection

IEEE Transactions on Neural Networks
Contextual classifier ensembles

BIS'07 Proceedings of the 10th international conference on Business information systems
Gene expression analysis of leukemia samples using visual interpretation of small ensembles: a case study

PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
A comparison of three voting methods for bagging with the MLEM2 algorithm

IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Dependency bagging

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We experimentally evaluate randomization-based approachesto creating an ensemble of decision-tree classifiers.Unlike methods related to boosting, all of the eightapproaches considered here create each classifier in an ensembleindependently of the other classifiers. Experimentswere performed on 28 publicly available datasets, usingC4.5 release 8 as the base classifier. While each of the otherseven approaches has some strengths, we find that none ofthem is consistently more accurate than standard baggingwhen tested for statistical significance.