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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25527

Title: Fuzzy-rough cognitive networks
Authors: Nápoles, Gonzalo
Mosquera, Carlos
Falcon, Rafael
Grau, Isel
Bello, Rafael
Vanhoof, Koen
Issue Date: 2018
Citation: NEURAL NETWORKS, 97, p. 19-27
Abstract: Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the rough information granules are built. In this paper, we cast the RCN model within the framework of fuzzy rough sets in an attempt to eliminate the need for a userspecified similarity threshold while retaining the model’s discriminatory power. As far as we know, this is the first study that brings fuzzy sets into the domain of rough cognitive mapping. Numerical results in the presence of 140 well-known pattern classification problems reveal that our approach, referred to as FuzzyRough Cognitive Networks, is capable of outperforming most traditional classifiers used for benchmarking purposes. Furthermore, we explore the impact of using different heterogeneous distance functions and fuzzy operators over the performance of our granular neural network.
URI: http://hdl.handle.net/1942/25527
DOI: 10.1016/j.neunet.2017.08.007
ISI #: 000416454000004
ISSN: 0893-6080
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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