Browsing by Author "Sun, Quan"
Now showing items 1-5 of 9
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Bagging ensemble selection
(Springer, 2011)Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The ... -
Bagging ensemble selection for regression
(Springer, 2012)Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on ... -
Evolving artificial datasets to improve interpretable classifiers
(IEEE, 2014)Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple ... -
Full model selection in the space of data mining operators
(ACM, 2012)We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for ... -
Hierarchical meta-rules for scalable meta-learning
(Springer Verlag, 2014)The Pairwise Meta-Rules (PMR) method proposed in [18] has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), ...
