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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21459

Title: Hybrid Model Based on Rough Sets Theory and Fuzzy Cognitive Maps for Decision-Making
Authors: Napoles, Gonzalo
Grau, Isel
Vanhoof, Koen
Bello, Rafael
Issue Date: 2014
Publisher: Springer International Publishing
Citation: Kryszkiewicz, Marzena; Cornelis, Chris; Ciucci, Davide; Medina-Moreno, Jesús; Motoda, Hiroshi; Raś, Zbigniew W. (Ed.). Rough Sets and Intelligent Systems Paradigms: Second International Conference, RSEISP 2014, Held as Part of JRS 2014, Granada and Madrid, Spain, July 9-13, 2014. Proceedings, p. 169-178
Series/Report: Lecture Notes in Computer Science
Series/Report no.: 8537
Abstract: Decision-making could be defined as the process to choose a suitable decision among a set of possible alternatives in a given activity. It is a relevant subject in numerous disciplines such as engineering, psychology, risk analysis, operations research, etc. However, most real-life problems are unstructured in nature, often involving vagueness and uncertainty features. It makes difficult to apply exact models, being necessary to adopt approximate algorithms based on Artificial Intelligence and Soft Computing techniques. In this paper we present a novel decision-making model called Rough Cognitive Networks. It combines the capability of Rough Sets Theory for handling inconsistent patterns, with the modeling and simulation features of Fuzzy Cognitive Maps. Towards the end, we obtain an accurate hybrid model that allows to solve non-trivial continuous, discrete, or mixed-variable decision-making problems.
URI: http://hdl.handle.net/1942/21459
DOI: 10.1007/978-3-319-08729-0_16
ISI #: 000347192900016
ISBN: 9783319087283
ISSN: 0302-9743
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2016
Appears in Collections: Research publications

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