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View all- Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
Comparing ensemble learning approaches in genetic programming for classification with unbalanced data
Pages 135 - 136
Abstract
This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble.
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- Comparing ensemble learning approaches in genetic programming for classification with unbalanced data
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July 2013
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Association for Computing Machinery
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Published: 06 July 2013
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GECCO '13
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GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands
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View all- Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
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