2007 · 32 citations · 19 references
ComputingComputer ScienceData ScienceDecision TheoryEnsemble PerformanceMachine LearningClassificationDecision-level FusionDiversity MetricsEnsemble LearningEnsemble MethodsSupport Vector MachinesClassifier Ensemble FusionClassifier FusionDiversity MeasuresEmpirical EvaluationEnsemble ClassificationEnsemble Learning TheoryMajority VotingMargin Theory
The main goal of this paper is to investigate the relationship between two theories widely applied to explain the success of classifiers fusion: diversity measures and margin theory. In order to achieve this, we realized an empirical study which evaluates some classical measures related to these two theories with respect to ensembles accuracy. In particular, this study revealed valuable insights on how these two theories can influence each other, and how the application of margin based measures can be useful for the evaluation and selection of ensembles of classifiers with majority voting.
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Leo Breiman · Machine Learning · 1996 · 17K citations · Full text
Leo Breiman · Machine Learning · 1996 · 16.4K citations · Full text