Creating diversity in ensembles using artificial data

By Prem Melville, Raymond J. Mooney. Information Fusion, 2005

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How researchers see this paper

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AdaBoost with SVM-based component classifiers (2008, methodology): “AdaBoostSVM can be seen as a proof of concept of the idea proposed in Valentini and Dietterich [2004.”
Diverse ensembles for active learning (2004, methodology): “Decorate This section summarizes theDecorate algorithm; for further details see (Melville & Mooney, 2003; Melville & Mooney, 2004).”
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles (2016, methodology): “This is often accomplished by resampling existing training data for each member model [27] or by producing artificial data that encourages new models to be decorrelated with the existing ensemble [21].”
Graph-based approach for airborne light detection and ranging segmentation (2014, background): “Decorate w learns an ensemble of fifteen J48 tree classifiers with high diversity trained with specially constructed artificial training patterns (Melville and Mooney, 2004).”