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New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO

Authors : Pedro Villar, Alberto Fernández, Francisco Herrera

Published in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Publisher: Springer International Publishing

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Abstract

Fuzzy rule-based systems constitute a wide spread tool for classification problems, but several proposals may decrease its performance when dealing with multi-class problems. Among existing approaches, the FARC-HD algorithm has excelled as it has shown to achieve accurate and compact classifiers, even in the context of multi-class problems. In this work, we aim to go one step further to improve the behavior of the former algorithm by means of a “divide-and-conquer” approach, via binarization in a one-versus-one scheme. Besides, we will contextualize each binary classifier by adapting the database for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several datasets from KEEL dataset repository, shows the goodness of the proposed methodology.

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previous chapter Using a Genetic Algorithm for Selection of Starting Conditions for the EM Algorithm for Gaussian Mixture Models
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Footnotes
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Metadata
Title
On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO
Authors
Pedro Villar
Alberto Fernández
Francisco Herrera
Copyright Year
2016
Book
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-26227-7_13

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