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2016 | OriginalPaper | Chapter

Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm

Authors : Pedro Lopez-Garcia, Michał Woźniak, Enrique Onieva, Asier Perallos

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

The paper presents an approach to train combined classifiers based on feature space splitting and selection of the best classifier ensemble to each subspace of feature space. The learning method uses a hybrid algorithm that combines a Genetic Algorithm and Cross Entropy Method. The proposed approach was evaluated on the basis of the comprehensive computer experiments run on balanced and imbalanced datasets, and compared with Cluster and Selection algorithm, improving the results obtained by this technique.

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Footnotes
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Metadata
Title
Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm
Authors
Pedro Lopez-Garcia
Michał Woźniak
Enrique Onieva
Asier Perallos
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_62

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