Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection

Authors
Alberto Fernández, Mikel Galar, José Antonio Sanz, Humberto Bustince, Francisco Herrera
Corresponding Author
Alberto Fernández
Available Online June 2015.
DOI
https://doi.org/10.2991/ifsa-eusflat-15.2015.144How to use a DOI?
Keywords
Fuzzy rule based classification systems, multi-classification, One-vs-One, pairwise learning, dynamic classifier selection.
Abstract
classification based on the One-vs-One decomposition strategy has shown a high quality for addressing those problems with multiple classes, even if the learning model enables the discrimination among several concepts. The main phase of the pairwiselearning is the decision process, where the outputs of the binary classifiers are combined to give a single output. Recently, it has been shown that standard decision techniques do not take into account the influence of the non-competent classifiers, i.e. those that were not trained using the class of the query example, and this can deteriorate the performance of the model. In accordance with the former, a “Dynamic Classifier Selection” for the Onevs- One approach was proposed to alleviate this issue. It basically consists of finding those classifiers whose outputs are closest to the input example, and thus remove those ones which are not related with it. In this work, we want to analyse the goodness for the former approach using a fuzzy-type baseline classifier. Experimental results show that there is in fact a significant leap in the global performance when this model is applied, both versus the standard fuzzy rule based classification system, and the One-vs-One learning approach.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Alberto Fernández
AU  - Mikel Galar
AU  - José Antonio Sanz
AU  - Humberto Bustince
AU  - Francisco Herrera
PY  - 2015/06
DA  - 2015/06
TI  - Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1020
EP  - 1026
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.144
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.144
ID  - Fernández2015/06
ER  -