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.
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Nápoles, G., Grau, I., Vanhoof, K., Bello, R. (2014). Hybrid Model Based on Rough Sets Theory and Fuzzy Cognitive Maps for Decision-Making. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_16
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DOI: https://doi.org/10.1007/978-3-319-08729-0_16
Publisher Name: Springer, Cham
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