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

Keywords

Boosting (machine learning), AdaBoost, Artificial intelligence, Computer science, Machine learning

References

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Cited by

• Simulation of Markovian models using bootstrap methodRicardo M. Czekster, Paulo Fernandes, Afonso Sales — 2010
• Evaluating the C-section Rate of Different Physician Practices: Using Machine Learning to Model Standard PracticeRich Caruana, Radu Stefan Niculescu, R. Bharat Rao — 2003
• Logitboost of Simple Bayesian ClassifierSotiris Kotsiantis, Panayiotis Pintelas — 2005
• Sequence Learning from Data with Multiple LabelsMark Dredze, Partha Talukdar, Koby Crammer — 2009
• A Simple Algorithm for Learning Stable MachinesS. Andonova, André Elisseeff, Theodoros Evgeniou — 2002
• A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samplesFlorent Martin — 2006
• An Online Ensemble of ClassifiersS. Kotsiantis, P. Pintelas — 2004
• Diversity-Based Weighting Schemes for Clustering EnsemblesFrancesco Gullo, Andrea Tagarelli, Sergio Greco — 2009
• DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin ModificationsCristina González Beilfuss — 2015
• Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifactsいつき 西村 — 2016
• A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of qualityAntonio Gracia Berná — 2014
• The Protein Structure Prediction Module of the Prot-Grid Information SystemDimitrios Vogiatzis, Dimitrios Frosyniotis, George Α. Papadopoulos — 2003
• A Study on Efficacy of Ensamble Methods for Classification LearningThomas L. Ngo-Yel, Abhijit Dutt — 2009
• Development and application of soft computing and data mining techniques in hot dip galvanisingAndrés Sanz-García — 2013
• Kernel methods for remote sensing data analysisPeter Gehler, Bernhard Scholkopf — 2009
• Bagging KNN Classifiers using Different Expert Fusion StrategiesAmer. J. AlBaghdadi, Fuad M. Alkoot — 2005
• Text Document Categorization by Machine LearningZeynel Sendur — 2008
• Heuristic subset clustering for consideration set analysisDing Yuan — 2007
• Unique classifier selection approach for bagging algorithmA. B. M. Shawkat Ali — 2007
• Improved Boosting Performance by Exclusion of Ambiguous Positive ExamplesMiroslav Kobetski, Josephine Sullivan — 2013

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