Neural networks and the bias/variance dilemma
Neural Computation
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Introduction to Machine Learning
Introduction to Machine Learning
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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We are proposing to use the Nondominated Sorting Genetic Algorithm II (NSGA-II) for optimizing a hydrological forecasting model of 800 simultaneous streamflow predictors. The optimization is based on the selection of the best 48 predictors from the 800 that jointly define the "best" ensemble in terms of two probabilistic criteria. Results showed that the difficulties in simplifying the ensembles mainly originate from the preservation of the system reliability. We conclude that Pareto fronts generated with NSGA-II allow the development of a decision process based explicitly on the trade-off between different probabilistic properties. In other words, evolutionary multiobjective optimization offers more flexibility to the operational hydrologists than a priori methods that produce only one selection.