Abstract
Compensatory Fuzzy Logic (CFL) is a logical system that enables an optimal way of modeling knowledge. Its axiomatic character enables the work of natural language translation of logic, so it is used in knowledge discovery and decision-making.Obtaining LDC predicates with high values of truth is a general and flexible approach that can be used to discover patterns and new knowledge from data. This work proposes a method for knowledge discovery from obtaining LDC predicates, to obtain different structures of knowledge using a metaheuristic approach. A series of experiments and results descriptions of certains advantages for representing several patterns and tendencies from data is used to prove the proposed method.
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Alonso, M.M., Andrade, R.A.E., Batista, V.L., Suárez, A.R. (2014). Discovering Knowledge by Fuzzy Predicates in Compensatory Fuzzy Logic Using Metaheuristic Algorithms. In: Espin, R., Pérez, R., Cobo, A., Marx, J., Valdés, A. (eds) Soft Computing for Business Intelligence. Studies in Computational Intelligence, vol 537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53737-0_11
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DOI: https://doi.org/10.1007/978-3-642-53737-0_11
Publisher Name: Springer, Berlin, Heidelberg
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