Compensatory Fuzzy Logic Genetic Algorithm for Classification Problems: A Case Study

• Autores: José Fernando Padrón-Tristán, Laura Cruz-Reyes , Rafael Alejandro Espín Andrade, Carlos Eric Llorente-Peralta, Fausto Antonio Balderas-Jaramillo, Jessica González-San-Martín
• Localización: Computación y Sistemas (CyS), ISSN 1405-5546, ISSN-e 2007-9737, Vol. 28, Nº. 3, 2024, págs. 1257-1274
• Idioma: inglés
• DOI: 10.13053/cys-28-3-5183
• Enlaces
  • Texto completo
• Resumen

  • Abstract: This article presents an approach for creating fuzzy predicates using genetic algorithms. The proposed method incorporates an internal genetic algorithm to optimize the membership functions of the linguistic variables involved in the discovered predicates, taking advantage of statistical data for the initialization of the population and taboo and weighted roulettes for the construction of the predicates. The generation of fuzzy predicates is based on the implication and equivalence operators, as well as on deductive structures, such as modus ponens. Furthermore, the evaluation of predicates on data sets is based on For All and Exists quantifier operators, which also guide the search for the best predicates according to their truth values. Furthermore, the popular Iris database is used as a case study to demonstrate the effectiveness and applicability of this approach.

• Referencias bibliográficas
  • Cisneros, L.,Rivera, G.,Florencia, R.,Sánchez-Solís, J. P.. (2023). Fuzzy optimisation for business analytics: A bibliometric analysis. Journal...
  • Xexéo, G.,Braga, A.. (2008). Handbook of Research on Fuzzy Information Processing in Databases. IGI Global.
  • Pérez-Pueyo, R.. (2005). Procesado y optimización de espectros Raman mediante técnicas de lógica difusa: aplicación a la identificación de...
  • Padrón-Tristán, J. F.,Cruz-Reyes, L.,Espín-Andrade, R. A.,Llorente-Peralta, C. E.. (2021). A brief review of performance and interpretability...
  • Zadeh, L. A.. (1965). Information and control. Fuzzy sets. 8. 338
  • Serov, V. V.,Sokolov, I. V.,Budnik, A. A.. (2019). Applied calculus of fuzzy predicates for the formalization of knowledge. IOP Conference...
  • Espin-Andrade, R. A.,Gonzalez, E.,Pedrycz, W.,Fernandez, E.. (2016). An interpretable logical theory: the case of compensatory fuzzy logic....
  • Espín-Andrade, R. A.,Bataller, A. C.,Marx-Gomez, J.,Racet, A.. (2011). Fuzzy semantic transdisciplinary knowledge discovery approach for business...
  • Galende, M.,Sainz, G. I.,Fuente, M. J.. (2011). Accuracy-interpretability trade-off for precise fuzzy modeling using simple indices. Application...
  • Llorente-Peralta, C. E.,Cruz-Reyes, L.,Espín-Andrade, R. A.. (2021). Knowledge discovery using an evolutionary algorithm and compensatory...
  • González-Ramírez, C. M.. (2017). Aproximación al concepto de inferencia desde dos modelos de comprensión: modelo estratégico y modelo de construcción...
  • Bunge, M.. (2000). La investigación científica: su estrategia y su filosofía. Siglo XXI.
  • Mamdani, E. H.,Assilian, S.. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine...
  • Takagi, T.,Sugeno, M.. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems,...
  • Galdi, P.,Tagliaferri, R.. (2018). Data mining: accuracy and error measures for classification and prediction. Encyclopedia of bioinformatics...
  • Fernández-Cervantes, V.,García, A.,Ramos, M. A.,Méndez, A.. (2015). Facial geometry identification through fuzzy patterns with RGBD sensor....
  • Silva, D.,Manzo-Martínez, A.,Gaxiola, F.,Gonzalez-Gurrola, L.,Ramírez-Alonso, G.. (2022). Analysis of CNN architectures for human action recognition...
  • Pach, F. P.,Gyenesei, A.,Abonyi, J.. (2008). Compact fuzzy association rule-based classifier. Expert Systems with Applications. 34. 2406
  • Binu, R.,Isaac, P.. (2021). Neutrosophic Operational Research. Springer. Cham.
  • Zadeh, L. A.. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences. 8. 199-249
  • Gullo, F.. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia. 62. 18-22
  • Bigand, A.,Colot, O.. (2016). Membership function construction for interval-valued fuzzy sets with application to Gaussian noise reduction....
  • Comas, D. S.,Meschino, G. J.,Nowé, A.,Ballarin, V. L.. (2017). Discovering knowledge from data clustering using automatically-defined interval...
  • Zadeh, L. A.. (1989). Development of Probabilistic and Possebilistic Approaches to Approximate Reasoning and Its Applications. University...