Fuzzy Activation of Rough Cognitive Ensembles Using OWA Operators

Marilyn Bello, Gonzalo Nápoles, Ivett Fuentes, Isel Grau, Rafael Falcon, Rafael Bello, Koen Vanhoof

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


Rough Cognitive Ensembles (RCEs) can be defined as a multiclassifier system composed of a set of Rough Cognitive Networks (RCNs), each operating at a different granularity degree. While this model is capable of outperforming several traditional classifiers reported in the literature, there is still room for enhancing its performance. In this paper, we propose a fuzzy strategy to activate the RCN input neurons before performing the inference process. This fuzzy activation mechanism essentially quantifies the extent to which an object belongs to the intersection between its similarity class and each granular region in the RCN topology. The numerical simulations have shown that the improved ensemble classifier significantly outperforms the original RCE model for the adopted datasets. After comparing the proposed model to 14 well-known classifiers, the experimental evidence confirms that our scheme yields very promising classification rates.
Original languageEnglish
Title of host publicationUncertainty Management with Fuzzy and Rough Sets
Subtitle of host publicationRecent Advances and Applications
EditorsRafael Bello
Place of PublicationCham
Number of pages19
ISBN (Electronic)978-3-030-10463-4
ISBN (Print)978-3-030-10462-7
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameStudies in Fuzziness and Soft Computing (STUDFUZZ)
ISSN (Print)1434-9922
ISSN (Electronic)1860-0808


  • Ensemble learning
  • Fuzzy activation mechanism
  • Granular Computing
  • Pattern classification
  • Rough cognitive maps


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