Popular Ensemble Methods: An Empirical Study

Keywords

Boosting (machine learning), Overfitting, Artificial intelligence, Decision tree, Computer science

References

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• Ensembles as a Sequence of ClassifiersLars Asker, Richard Maclin — 1997
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• C4.5: Programs for Machine Learning (書評)重郎 金田 — 1995
• Bias Plus Variance Decomposition for Zero-One Loss FunctionsRon Kohavi, David H. Wolpert — 1996
• An Empirical Evaluation of Bagging and BoostingRichard Maclin, David W. Opitz — 1997
• Boosting the margin: A new explanation for the effectiveness of voting methodsPeter L. Bartlett, Yoav Freund, Wee Sun Lee — 1997
• Combining forecasts: A review and annotated bibliographyRobert T. Clemen — 1989
• Hybrid system for protein secondary structure prediction.Xiru Zhang, Jill P. Mesirov, David L. Waltz — 1992
• Optimal Linear Combinations of Neural NetworksS. Hashem — 1997
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• UCI Repository of machine learning databasesCatherine Blake — 1998
• The Strength of Weak LearnabilityRobert E. Schapire — 1989
• Boosting in the Limit: Maximizing the Margin of Learned EnsemblesAdam J. Grove, Dale Schuurmans — 1998
• Boosting Decision TreesHarris Drucker, Corinna Cortes — 1995
• Synergy of Clustering Multiple Back Propagation NetworksWilliam Lincoln, Josef Skrzypek — 1989
• Boosting Classifiers RegionallyRichard Maclin — 1998

Cited by

• Political Language in EconomicsZubin Jelveh, Bruce Kogut, Suresh Naidu — 2015
• Multiclass Adaboost Based on an Ensemble of Binary AdaBoostsHasan Fleyeh, Erfan Davami — 2013
• Mitigation of Catastrophic Interference in Neural Networks and Ensembles using a Fixed Expansion LayerRobert Coop — 2013
• Identification de complexes protéine-protéine par combinaison de classifieurs. Application à Escherichia ColiThomas Bourquard, Damien M. de Vienne, Jérôme Azé — 2013
• Algorithms to Explore the Structure and Evolution of Biological NetworksCarleton L. Kingsford, Saket Navlakha — 2010
• A Study on Efficacy of Ensamble Methods for Classification LearningThomas L. Ngo-Yel, Abhijit Dutt — 2009
• Identifying Product Entities in text with Conditional Random FieldsD.E. van Kuppevelt — 2014
• Feature set decomposition for decision treesLior Rokach, Oded Maimon — 2005
• Development and application of soft computing and data mining techniques in hot dip galvanisingAndrés Sanz-García — 2013
• A Ranked Subspace Learning Method for Gene Expression Data ClassificationHaibo He, Xiaoping Shen — 2007
• Text Document Categorization by Machine LearningZeynel Sendur — 2008
• Liberal or Conservative: Evaluation and Classification with Distribution as Ground Truth.Daniel Xiaodan Zhou — 2013
• Méthodes statistiques pour l'évaluation et la reconfiguration des réseaux de suivi de la qualité de l'eau de surface.Bahaa Khalil — 2010
• Use of Shape Representation and Similarity in Classification of UXO in Magnetometry DataJim McDonald, David W. Opitz — 2004
• Empirical Study on Generalization of NN in Respect to Network ComplexitySang-Bong Park, Cheol Hoon Park — 1998
• Machine Learning Methods for High-Dimensional Imbalanced Biomedical DataTao Yang — 2013
• Segmentación de lesiones de esclerosis múltiple en imágenes de RM de alto campo.Florencia L. Rodrigo — 2013
• Using Optimization-Based Classification Method for Massive DatasetsYi Peng, Gang Kou, Yong Shi — 2005
• Extended Trust-Tech Methodology For Nonlinear Optimization: Analyses, Methods And ApplicationsBin Wang — 2012
• Boosting SVM Classifiers with Logistic RegressionYuan‐chin Ivan Chang — 2003

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