Original Contribution: Stacked generalization
Neural Networks
Artificial Intelligence Review - Special issue on lazy learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business
Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business
Design of local fuzzy models using evolutionary algorithms
Computational Statistics & Data Analysis
Multicriteria decision making (MCDM): a framework for research and applications
IEEE Computational Intelligence Magazine
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
International Journal of Approximate Reasoning
Fast meta-models for local fusion of multiple predictive models
Applied Soft Computing
Using an ensemble of classifiers to audit a production classifier
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Flex-GP: genetic programming on the cloud
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Learning regression ensembles with genetic programming at scale
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In the not so distant future, we expect analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, crowdservicing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models. In this new context, the critical question will be model ensemble selection and fusion, rather than model generation. We address this issue by proposing customized model ensembles on demand, inspired by Lazy Learning. In our approach, referred to as Lazy Meta-Learning, for a given query we find the most relevant models from a DB of models, using their meta-information. After retrieving the relevant models, we select a subset of models with highly uncorrelated errors. With these models we create an ensemble and use their meta-information for dynamic bias compensation and relevance weighting. The output is a weighted interpolation or extrapolation of the outputs of the models ensemble. Furthermore, the confidence interval around the output is reduced as we increase the number of uncorrelated models in the ensemble. We have successfully tested this approach in a power plant management application.