Rough cognitive ensembles

Gonzalo Nápoles*, Rafael Falcon, Elpiniki Papageorgiou, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Rough Cognitive Networks are granular classifiers stemming from the hybridization of Fuzzy Cognitive Maps and Rough Set Theory. Such cognitive neural networks attempt to quantify the impact of rough granular constructs (i.e., the positive, negative and boundary regions of a target concept) over each decision class for the problem at hand. In rough classifiers, determining the precise granularity level is crucial to compute high prediction rates. Regrettably, learning the similarity threshold parameter requires reconstructing the information granules, which may be time-consuming. In this paper, we put forth a new multiclassifier system classifier named Rough Cognitive Ensembles. The proposed ensemble employs a collection of Rough Cognitive Networks as base classifiers, each operating at a different granularity level. This allows suppressing the requirement of learning a similarity threshold. We evaluate the granular ensemble with 140 traditional classification datasets using different heterogeneous distance functions. After comparing the proposed model to 15 well-known classifiers, the experimental evidence confirms that our scheme yields very promising classification rates.
Original languageEnglish
Pages (from-to)79-96
Number of pages18
JournalInternational Journal of Approximate Reasoning
Publication statusPublished - 2017
Externally publishedYes


  • Machine learning
  • Granular computing
  • Rough set theory
  • Fuzzy cognitive maps
  • Rough cognitive networks
  • Ensemble learning

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