Greedy regression ensemble selection: Theory and an application to water quality prediction
Information Sciences: an International Journal
MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Design and analysis of the KDD cup 2009: fast scoring on a large orange customer database
ACM SIGKDD Explorations Newsletter
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining multiple classification or regression models using genetic algorithms
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Web spam classification: a few features worth more
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality
StackTIS: A stacked generalization approach for effective prediction of translation initiation sites
Computers in Biology and Medicine
On selecting additional predictive models in double bagging type ensemble method
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Ensemble pruning for text categorization based on data partitioning
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Expert pruning based on genetic algorithm in regression problems
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Bagging ensemble selection for regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
On the effect of calibration in classifier combination
Applied Intelligence
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
Ensemble selection for feature-based classification of diabetic maculopathy images
Computers in Biology and Medicine
Combining multiple predictive models using genetic algorithms
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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We investigate four previously unexplored aspects of ensemble selection, a procedure for building ensembles of classifiers. First we test whether adjusting model predictions to put them on a canonical scale makes the ensembles more effective. Second, we explore the performance of ensemble selection when different amounts of data are available for ensemble hillclimbing. Third, we quantify the benefit of ensemble selection's ability to optimize to arbitrary metrics. Fourth, we study the performance impact of pruning the number of models available for ensemble selection. Based on our results we present improved ensemble selection methods that double the benefit of the original method.