A fuzzy activation mechanism for rough cognitive ensembles

Rough Cognitive Ensembles (RCEs) has recently emerged as a granular 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 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 improved classification rates.