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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23693

Title: Rough cognitive ensembles
Authors: Napoles Ruiz, Gonzalo
Falcon, Rafael
Papageorgiou, Elpiniki
Bello, Rafael
Vanhoof, Koen
Issue Date: 2017
Abstract: 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.
Notes: Napoles, G (reprint author), Hasselt Univ, Fac Business Econ, Hasselt, Belgium. gonzalo.napoles@uhasselt.be
URI: http://hdl.handle.net/1942/23693
Link to publication: https://www.researchgate.net/publication/315613667_Rough_Cognitive_Ensembles?ev=prf_high
DOI: 10.1016/j.ijar.2017.03.011
ISI #: 000401396400006
ISSN: 0888-613X
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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