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Un modelo para la extracción de conocimiento en base de datos, mediante cómputo evolutivo, automátas finitos deterministas y reglas difusas

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dc.contributor.authorArcón Pineda, Aidys
dc.date.accessioned2019-12-16T15:54:01Z
dc.date.available2019-12-16T15:54:01Z
dc.date.issued2019
dc.description.abstractEl objetivo de esta tesis es el desarrollo de un método para la extracción de conocimiento en grandes bases de datos. Hoy en día se genera a través de la tecnología grandes cantidades de datos, tanto en volumen como en la dimensionalidad de sus variables. En este sentido, es necesario resaltar que la manipulación de los datos con un número elevado de variables presenta un problema para las técnicas tradicionales. Por otra parte, el conjunto de soluciones alternativas es tan elevado que la obtención de un óptimo global es inalcanzable en un tiempo razonable. Por tanto, es imprescindible emplear técnicas basadas en meta heurísticas que han demostrado ser una alternativa en la solución de problemas de gran complejidad por su aplicabilidad y eficiencia. El presente trabajo propone un nuevo modelo EKCEAD (A model for the extraction of knowledge in Databases through evolutionary computation, finite deterministic automata and fuzzy rules), para la extracción de conocimiento en grandes bases de datos en forma de reglas difusas mediante el desarrollo de varias herramientas orientadas a procesos de búsqueda y optimización que utiliza las ventajas de autómatas finitos en la obtención de la mejor solución. El cual se prueba con casos reales comparándolo con otros métodos similares referenciados en la literatura, los resultados obtenidos han sido validados mediante el uso de pruebas estadísticas no paramétricas, que muestran un buen desempeño en términos de precisión e interpretabilidad. Los resultados reflejan la bondad del modelo propuesto permitiendo que sea recomendado en la extracción de conocimiento en base de datos e incentivando su uso en futuras investigaciones.spa
dc.description.abstractThe objective of this thesis is the development of a method for extracting knowledge in large databases. Today, large amounts of data are generated through technology, both in volume and in the dimensionality of their variables. In this sense, it should be emphasized that the manipulation of data with a large number of variables presents a problem for traditional techniques. On the other hand, the set of alternative solutions is high that obtaining an overall optimal is unattainable in a reasonable time. Therefore, it is essential to employ heuristic meta-based techniques that have proven to be an alternative in solving problems of great complexity because of their applicability and efficiency. This work proposes a new model EKCEAD (A model for the extraction of knowledge in Databases through evolutionary computation, finite deterministic automata and fuzzy rules), for knowledge extraction in large databases in the form of fuzzy rules by developing various tools oriented to search and optimization processes that uses the advantages of finite automata in obtaining the best solution. Which is tested against real-world cases against other similar methods referenced in the literature, the results obtained have been validated using nonparametric statistical tests, which show good performance in terms of accuracy and interpretability? The results reflect the goodness of the proposed model by allowing it to be recommended in the extraction of knowledge in database and incentivizing its use in future research.eng
dc.identifier.urihttps://hdl.handle.net/20.500.12442/4476
dc.language.isospaspa
dc.publisherEdiciones Universidad Simón Bolívarspa
dc.publisherFacultad de Ingenieríasspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAlgoritmo evolutivospa
dc.subjectAutómatas finitos deterministasspa
dc.subjectLógica difusaspa
dc.subjectExtracción de conocimientospa
dc.subjectBase de reglas difusasspa
dc.subjectOptimizaciónspa
dc.subjectEvolutionary algorithmeng
dc.subjectDeterministic finite automatonseng
dc.subjectFuzzy logiceng
dc.subjectKnowledge extractioneng
dc.subjectFuzzy rule baseeng
dc.subjectOptimizationeng
dc.titleUn modelo para la extracción de conocimiento en base de datos, mediante cómputo evolutivo, automátas finitos deterministas y reglas difusasspa
dc.typeOtherspa
dcterms.referencesA. Gokil, & S. Rajalakshmi. (2014). Weighted Quamtum Particle Swarm Optimization(WQPSO) and PSO algorithm to association Rule Minng and clustering. Assian Journal of Information Technology, 13(10), 582–587.eng
dcterms.referencesAbe. S., & Lan M.-S. (1995). A method for fuzzy rules extraction directly from numerical data and its application to pattern classi- fication. IEEE Transactions on Fuzzy Systems, 3(1):, 18–28.eng
dcterms.referencesAbe.S., & Thawonmas, R. (1997). A fuzzy classifier with ellipsoidal regions. IEEE Transactions on Fuzzy Systems, 5(3):, 358–368.eng
dcterms.referencesAbonyi. J., Roubos ,J. A., & Szeifert. F. (2003). Data driven genera tion of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization. International Journal of Approximate Reasoning. International Journal of Approximate Reasoning, 32(1):, 1–21.eng
dcterms.referencesAbonyi J., R. J. A. y S. F. (2003). Data driven genera tion of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization. International Journal of Approximate Reasoning. International Journal of Approximate Reasoning, 32(1):, 1–21.eng
dcterms.referencesAgrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, I. (1996). Fast Discovery of Association Rules, in Advances in Knowladge Discovery and Data Mining (pp. 307–328). pp. 307–328. California.eng
dcterms.referencesAguilar. L. (2019). Analisis comparativo en la implementacion de la red neuronal backpropagation usando el metodo de componentes principales y el metodo clasico. Universidad nacional de piura escuela de posgrado.spa
dcterms.referencesAl-Omairi, L., Abawajy, J., Chowdhury, M., & Al-Quraishi, T. (2019). High-Dimensionality Graph Data Reduction Based on Proposing A New Algorithm. EpiC Series Computing, 63, 1--10. https://doi.org/10.29007/h232eng
dcterms.referencesAlcalá R., Alcalá-Fdez J., H. F. y O. J. (2007). Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation. International Journal of Approximate Reasoning, 44(1):, 45–64.eng
dcterms.referencesAlhajj, R., & Kaya, M. (2008). Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. Journal of Intelligent Information Systems, 31(3), 243–264. https://doi.org/10.1007/s10844-007-0044-1eng
dcterms.referencesAlhusaini, N., Karmoshi, S., Hawbani, A., Jing, L., Alhusaini, A., & Al-sharabi, Y. (2019). LUIM: New Low-Utility Itemset Mining Framework. IEEE Access, 7, 100535–100551. https://doi.org/10.1109/access.2019.2929082eng
dcterms.referencesAlvarez, A., Boente, G., & Kudraszow, N. (2019). Robust sieve estimators for functional canonical correlation analysis. Journal of Multivariate Analysis, 170, 46–62. https://doi.org/10.1016/j.jmva.2018.03.003eng
dcterms.referencesAsadi, A., Afzali, M., Shojaei, A., & Sulaimani, S. (2012). New binary PSO based method for finding best thresholds in association rule mining. Life Science Journal, 9(4), 260–264.eng
dcterms.referencesBäck, T. (1996). Evolutionary algorithms in theory and practice. Ox- Ford University Press.eng
dcterms.referencesBäck, T., Fogel. D, & Michalewicz. Z. (1997). Handbook of evolutionary computation. . . Oxford University Press.eng
dcterms.referencesBäck, T., & Schwefel H. (1991). Extended selection mechanisms in genetic algorithms. En Proc. Fourth International Conference on Genetic Algorithms (ICGA’91), páginas 2–9. San Diego, EE.UU.eng
dcterms.referencesBaker J. E. (1987). Reducing bias and inefficiency in the selection algorithm. En Grefenstette J. J. (Ed.) Proceedings of the 2nd In- ternational Conference on Genetic Algorithms (ICGA’87),. páginas 14–21. . Lawrence Erlbaum Associates, Hillsdale, NJ, EE.UU.eng
dcterms.referencesBandyopadhyay, S., Saha, S., Maulik, U., & Deb, K. (2008). A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12(3), 269–283. https://doi.org/10.1109/TEVC.2007.900837eng
dcterms.referencesBerlanga, F. (2010). Aprendizaje de Sistemas Basados en Reglas difusas compactas y precisas con programacion genetica. Tesis Doctoral.Universidad de Jaen, Andalucia. España., Andalucia ,España.spa
dcterms.referencesBerlanga, F. J., Rivera, A. J., del Jesus, M. J., & Herrera, F. (2010). GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. Information Sciences, 180(8), 1183–1200. https://doi.org/10.1016/j.ins.2009.12.020eng
dcterms.referencesBhatt R. B. y Gopal M. (2005). On fuzzy-rough sets approach to feature selection. Pattern Recognition Letters, 26(7):, 965–975.eng
dcterms.referencesBouchachia A. Mittermeir R. (2007). Towards incremental fuzzy classifiers. Soft Computing, 11(2):, 193–207.eng
dcterms.referencesBrad, L. M., & Goldberg, D. E. (1995). Genetic Algorithms, Tournament Selection, and the Effects of Noise. Connect, 9, 13–13.eng
dcterms.referencesBreiman. L. (1984). Classification and Regression Trees. Monterey, Wadsworth and Brooks.eng
dcterms.referencesBreiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regresion Tress.eng
dcterms.referencesC., F., Hernández. J., & Ramírez. J. (2001). Learning MDL-guided Decision Trees for Constructor-Based Languages”. In Proceedings of 11th International Conference on Inductive Logic Programming, 39–50.eng
dcterms.referencesC. J. van Rijsbergen. (1979). “Information retrieval”. Butterworths.eng
dcterms.referencesCagnina, L. (2010). Optimización Mono y Multiobjetivo a través de una Heurística de Inteligencia Colectiva”,. Universidad Nacional de San Luis, Argentina,.spa
dcterms.referencesCalders, T., & Verwer, S. (2010). Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2), 277–292. https://doi.org/10.1007/s10618-010-0190-xeng
dcterms.referencesCardoso. P, Jesus. M, & Marquez. A. (20003). MONACO - Multi-Objective Network Optimisation Based on an ACO. (November).eng
dcterms.referencesCarmona, J. (2011). Descubrimiento de subgrupos mediante sistemas difusos evolutivos. II Jornadas Andaluzas de Informática, Canillas Del Aceituno (España), 30-35. Retrieved from http://simidat-web.ujaen.es/~simidat/sites/default/files/publicaciones/220.pdfspa
dcterms.referencesCasillas. J., Carse.B., & Bull. L. (2007). Fuzzy-XCS: a michigan ge- netic fuzzy system. IEEE Transactions on Fuzzy Systems, 15(4), 536–550.eng
dcterms.referencesCasillas. J, Cordón. O, del Jesus M., & Herrera. F. (2005). Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Transactions on Fuzzy Systems, 13(1): ., 13–29.eng
dcterms.referencesCasillas. J, Cordón. O, Herrera. F, & del Jesus. M. (2001). Genetic feature selection in a fuzzy rule-based classification system learn- ing process for high-dimensional problems. Information Sciences, 136(1-4):, : 135–157.eng
dcterms.referencesCasillas. J, Cordón. O, Herrera. F, & Magdalena. L. (2003). Interpretability Issues in Fuzzy Modeling. Of Studies in Fuzziness and Soft Computing. Springer-Verlag., 128.eng
dcterms.referencesCasillas, Cordón. O, Herrera. F., & Magdalena. L. (2003). Accuracy Improvements in Linguistic Fuzzy Modeling,. In Fuzziness and Soft Computing. Springer-Verlag., 129 of Stu.eng
dcterms.referencesCastellano G., Castiello C., F. A. M. y M. C. (2005). Knowledge discovery by a neuro-fuzzy modeling framework. Fuzzy Sets and Systems, 149(1):, 187–207.eng
dcterms.referencesCastellano G., Fanelli. A., & Mencar.c. (2002). A neuro-fuzzy network to generate humanunderstandable knowledge from data. Cognitive Systems Research, 3(2):, 125–144.eng
dcterms.referencesCastellano G. y Fanelli A. M. (2000). A staged approach for gener- ation and compression of fuzzy classification rules. En Proc. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2000), San Antonio, USA., 42–eng
dcterms.referencesCastro, D. (2004). Teoría de Autómatas, Lenguajes formales Y Gramatica (Universidad de Alcalá). Retrieved from https://portal.uah.es/portal/page/portal/GP_EPD/PG-MA-ASIG/PG-ASIG-78021/TAB42351/talfg_notes.pdfspa
dcterms.referencesCastro P. A. D. y Camargo H. A. (2004). Learning and optimiza- tion of fuzzy rule base by means of self-adaptive genetic algorithm. En Proc. EEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004) Budapest, Hungary., 1037– 1042.eng
dcterms.referencesChaiyaratana, N. (1997). Recent developments in evolutionary and genetic algorithms: theory and applications. (4), 270–277. https://doi.org/10.1049/cp:19971192eng
dcterms.referencesChakraborty. D., & Pal N.R. (2004). A neuro-fuzzy scheme for simul- taneous feature selection and fuzzy rule-based classification. IEEE Transactions on Neural Networks, 15(1):, 110–123.eng
dcterms.referencesChen M.-Y. y Linkens D. A. (2004). Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets and Systems, 142(2):, 243–265.eng
dcterms.referencesChen, W., Pradhan, B., Li, S., Shahabi, H., Rizeei, H. M., Hou, E., & Wang, S. (2019). Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis. Natural Resources Research, 28(4), 1239–1258. https://doi.org/10.1007/s11053-019-09465-weng
dcterms.referencesChi Z. y Yan H. (1996). ID3-Derived fuzzy rules and optimized defuzzification for handwritten numeral recognition. IEEE Trans- Actions on Fuzzy Systems, 4(1):, 24–31.eng
dcterms.referencesChi Z. Yan H. Pham T. (1996). Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific.eng
dcterms.referencesClark, P., & Niblett, T. (1989). The CN2 rule induction algorithm. Machine Learning, 3(4), 261–284. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.3672&rep=rep1&type=pdfeng
dcterms.referencesCohen, W. W. (2014). Fast Effective Rule Induction. In Machine Learning Proceedings 1995. https://doi.org/10.1016/b978-1-55860-377-6.50023-2eng
dcterms.referencesCombs. W. E., & Andrews. J. (1998). Combinatorial rule explosion eliminated by a fuzzy rule configuration. IEEE Transactions on Fuzzy Systems, 6(1):, 1–11.eng
dcterms.referencesCordón, O., Herrera, F., Hoffmann, F., and Magdalena, L. (2001). Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. World Scientific.eng
dcterms.referencesCordón, O., Herrera, F., Hoffmann, F., and Magdalena, L. (2004). Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. World Scientific.eng
dcterms.referencesCordón. O., del Jesus. M., & Herrera. F. (1999). A proposal on reasoning methods in fuzzy rule-based classification systems. In- Ternational Journal of Approximate Reasoning, 20(21–45).eng
dcterms.referencesCordon. o., Herrera. F, Magdalena. L., & Hoffmann. F. (2004). Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. In Fuzzy Sets and Systems (Vol. 141). https://doi.org/10.1016/S0165-0114(03)00262-8eng
dcterms.referencesCordón.O., del Jesus. M, F., H., & Lozano.M. (1999). MOGUL: A Methodology to Obtain Genetic fuzzy rule-based sys- tems Under the iterative rule Learning approach. International Journal of Intelligent Systems, 14 (11), 1123–1153.eng
dcterms.referencesCuervo, R. C. N., & Martínez, L. M. S. (2009). Herramienta software para el análisis de canasta de mercado sin selección de candidatos. Ingenieria e Investigacion, 29(1), 60–68.spa
dcterms.referencesDe Jong. K.A. (1975). An analysis of the behavior of a class of genetic adaptive systems, 1975. Univ. Michigan. https://doi.org/Microfilms Number 76-9381eng
dcterms.referencesDeb, K. (2001). Multi objetive optimization using evolutionary algortithms. Jhon Wiley & Sons.eng
dcterms.referencesDel jesus, M., González, P., & Herrera;Francisco. (2005). Induccion evolutiva multiobjetivo de reglas de descripcion de subgrupos en un problema de marketing. Actas Del IV Congreso Español Sobre Metaheuristicas, Algoritmosevolutivos y Bioinspirados MAEB2005, I 84-9732, 661–669. Retrieved from http://sci2s.ugr.es/publications/ficheros/deljesus_gonzalez_herrera_CEDI-MAEB05.pdfspa
dcterms.referencesDel Jesus, M., González, P., & Herrera, F. (2007). Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules. Intelligence in Multicriteria Decision Maeketing, (Mcdm), 50–57. Retrieved from http://sci2s.ugr.es/keel/pdf/algorithm/congreso/2007-delJesus-MCDM.pdfeng
dcterms.referencesDelJesus, Herrera, Mesonero, & Gonzalez. (2008). Algoritmo Evolutivo de Extracción de Reglas de Asociación aplicado a un Problema de Marketing. Ministerio de Ciencia y Tecnología. Retrieved from http://sci2s.ugr.es/keel/pdf/keel/congreso/maeb04-reglas-jaen-granada.pdfspa
dcterms.referencesDemšar. J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.eng
dcterms.referencesDerrac, J., Herrera, F., & García, S. (2010). Un Tutorial Metodológico para hacer Comparaciones Estadísticas con Tests No Paramétricos en Propuestas de Minería de Datos.spa
dcterms.referencesDietterich. T.G. (1998). aproximate statisticial test for comparing supervised classification learning algorithms. Neural Commpution, 10 (7).eng
dcterms.referencesDoerner, K. F., Hartl, R. F., & Reimann, M. (2001). Are {COMPETants} more competent for problem solving? - the case of a routing and scheduling problem. Genetic and Evolutionary Computation Conference, 802.eng
dcterms.referencesDonoso, Yezid; Fabregat, R. (2007). Multi-Objective Optimization in Computer Networks Using Metaheuristic. Aurbach Publications New York, Estados Unidos, 1 – 3.eng
dcterms.referencesDuong, Q. H., Fournier-Viger, P., Ramampiaro, H., Nørvåg, K., & Dam, T. L. (2018). Efficient high utility itemset mining using buffered utility-lists. Applied Intelligence, 48(7), 1859–1877. https://doi.org/10.1007/s10489-017-1057-2eng
dcterms.referencesE. L. Lawler, Jan Karel Lenstra, A. H. G. Rinnooy Kan, and D. B. S. (1985). The-Traveling-Salesman-Problem-A-Guided-Tour-of-Combinatorial-Optimization- (Wiley & C. Series in Discrete Mathematics & Optimization. Wiley Interscience, Eds.).eng
dcterms.referencesEschenauer H.A. (1988). Multicriteria Optimization Techniques for Highly Accurate Focusing Systems. In: Stadler W. (eds) Multicriteria Optimization in Engineering and in the Sciences. Mathematical Concepts and Methods in Science and Engineering, (vol 37.; M. Springer, Boston, Ed.).eng
dcterms.referencesFayyad. (1994). Branching on attribute values in decision tree generation”. In Proceedings of the Twelfth National Conference on Artificial Intelligence, 1, 601-606.eng
dcterms.referencesFayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996a). From Data Mining to Knowledge Discovery in Databases. AL MAGAZIN, 17(3), 37–54.eng
dcterms.referencesFayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996b). The KDD Process for Extracting Useful Knowledge from Volumes of Data. 39(11), 27–34.eng
dcterms.referencesFerri. C., Hernández. J., & Ramírez. J. (2002). Learning Decision Trees Using the Area Under the ROC Curve. In Proceedings of the 19th International Conference on Machine Learning, 139–146.eng
dcterms.referencesFlockhart, I.W. and Radcliffe, N. J. (1995). GA-MINER: Parallel data mining with hierarchical genetic algorithms. University of Edimburgh, UK.eng
dcterms.referencesFogel D. B. (1988). An evolutionary approach to the travelling sales man problem. Biological Cybernetics. 60(2):, 139–144.eng
dcterms.referencesFreitas, A. A. (1999). On rule interesting ness measures. Knowledge Based System, 15, 309–315.eng
dcterms.referencesFreitas, A. A. (2002). Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer.eng
dcterms.referencesFuchs, M. (1999). Genetic, Large Populations Are Not Always The Best Choice In Computation, Programming. Proceedings of the Genetic and Evolutionary Conference (GECCO-99)., 1033-1038. Orlando, FL,: Morgan-Kauffman,.eng
dcterms.referencesGambardella.L., Taillard. E, & Agazzi. G. (1999). MACS-VRPTW: un sistema de colonias múltiples para problemas de enrutamiento de vehículos con Windows de tiempo. Cite Ceer, 73–76.spa
dcterms.referencesGarcia. A. L., & Tsang. E. P. (2006). Simplifying Decision Trees Learned by Genetic Programming. In IEEE Congress on Evolutionary Computation, (2142–2148).eng
dcterms.referencesGarcía, S., Derrac, J., Cano, J. R., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 417–435. https://doi.org/10.1109/TPAMI.2011.142eng
dcterms.referencesGathercole, C., & Ross, P. (1997). Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming. . In: Koza, J. R., et al., Eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference., 111-118. Stanford University, CA: Morgan Kaufmann,.eng
dcterms.referencesGen, Mitsuo; Cheng, R. (2000). Genetic Algorithms & Engineering Optimization. Wiley-Interscience Publications, New York, Estados Unidos, 97–106.eng
dcterms.referencesGiordana.A., & Filippo. N. (1996). Search-intensive concept induction. Evolutionary Computation, (3(4)), 375-416. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.8986&rep=rep1&type=pdfeng
dcterms.referencesGoldberg. D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA.eng
dcterms.referencesGonzález A. Pérez R. (2001). Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B 31, 417–425.eng
dcterms.referencesGonzález J., Rojas I., Pomares H., Herrera L., Guillén A., P. J. R. F. (2007). Improving the accuracy while preserv- ing the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms. International Journal of Approximate Reasoning, 44(1):, 32–44.eng
dcterms.referencesGonzalez, P. (2007). Aprendizaje Evolutivo de Reglas Difusas para descripción de Subgrupos. Tesis Doctoral.Universidad de Granada.España.spa
dcterms.referencesGuerra Diego, A. (2015). Estado del arte y análisis de métodos de optimización de recursos en plantas de producción. Retrieved from https://uvadoc.uva.es/bitstream/10324/13230/1/TFG-P-272.pdfspa
dcterms.referencesGunal S. y Edizkan R. (2008). Subspace based feature selection for pattern recognition. Information Sciences, 178(19):, 3716–3726.eng
dcterms.referencesGupta, M. (2012). Application of Weighted Particle Swarm Optimization in Association Rule Mining. International Journal of Computer Sc Ience and Informatic, 1(3), 69–74.eng
dcterms.referencesHand, D. J. (1981). Driscrimination and Classification. (Wiley., Ed.). Chichester, U.K.eng
dcterms.referencesHasperue, W. (2013). Extraccion Del Conocimiento En Grandes Bases De Datos Utilizando Estrategias Adaptativas. In Journal of Chemical Information and Modeling (Vol. 53). https://doi.org/10.1017/CBO9781107415324.004spa
dcterms.referencesHastie, T., Tibshirani, R., & Friedman, J. (2009). Machine Learning. In Elements (Vol. 27). https://doi.org/10.1007/978-0-387-84858-7eng
dcterms.referencesHernandez, J., Ramirez, M., & Ferri, C. (2004). Introducción a la Mineria de Datos (D. F. Aragon, Ed.). madris: Hall, Pearson Prentice.eng
dcterms.referencesHernández Riaño, V. L., López Pereira, J. M., & Hernández Riaño, H. E. (2013a). Minimización del retraso extremo a extremo en redes Unicast utilizando técnicas metaheurísticas. Entre Ciencia e Ingeniería, (14), 66–71. Retrieved from http://biblioteca.ucp.edu.co/ojs/index.php/entreciencia/article/view/2218spa
dcterms.referencesHernández Riaño, V. L., López Pereira, J. M., & Hernández Riaño, H. E. (2013b). Minimización del retraso extremo a extremo en redes Unicast utilizando técnicas metaheurísticas. Entre Ciencia e Ingeniería, (14), 66–71.spa
dcterms.referencesHerrera F., Lozano, M., & L. Verdegay J. (1998). Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artificial. Intelligence Review 12: 265–319.eng
dcterms.referencesHincapie, R., Rios, C., & Gallego, R. (2004). Técnicas Heurísticas aplicadas al problema del cartero viajante (TSP). SCIENCIA ET TECHNICA, 24.spa
dcterms.referencesHo S. Y., Chen H. M., H. S. J. y C. T. K. (2004). (2004). Design of accurate classifiers with a compact fuzzy-rule base using an evolu- tionary scatter partition of feature space. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(, 1031–1044.eng
dcterms.referencesHolland . H. J. (1975). Adaptation in natural and artificial systems. Ann arbor: The University of Michigan Press.eng
dcterms.referencesHolland, J. H. (1975). Adaption in natural and artifical systems. (53), 1975.eng
dcterms.referencesHorn, J., Nafpliotis, N., & Goldberg, D. E. (2002). A niched Pareto genetic algorithm for multiobjective optimization. 1, 82–87. https://doi.org/10.1109/icec.1994.350037eng
dcterms.referencesHu Q., Yu D., L. J. y W. C. (2008). Neighborhood rough set based heterogeneous feature subset selection. Information Sciences, 178(18):, 3577–3594.eng
dcterms.referencesIredi, S., Merkle, D., & Middendorf, M. (2001). Bi-Criterion Optimization with Multi Colony Ant Algorithms. Lecture Notes in Computer Science 1993, 359–372. https://doi.org/10.1007/3-540-44719-9_25eng
dcterms.referencesIshibuchi. H., Nozaki. K., & Tanaka. H. (1994). Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets and Systems, 65:, 237–253.eng
dcterms.referencesIshibuchi. H, Nakashima. T, & Nii. M. (2004). Classification And Modeling With Linguistic Information Granules: Advanced Ap proaches To Linguistic Data Mining. Springer Verlag.eng
dcterms.referencesIshibuchi. H, Nozaki. K, & Tanaka. H. (1992). Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems. 52, 21–32.eng
dcterms.referencesIshibuchi .H., Yamamoto. T., & Nakashima.T. (2005). Hybridization of fuzzy gbml approaches for pattern classification problems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cy, 359–365.eng
dcterms.referencesIshibuchi H., Nozaki K., Y. N. y T. H. (1995). Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3(3):, 260–270.eng
dcterms.referencesIshibuchi H. y Nojima Y. (2007). Analysis of interpretability- accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics- based machine learning. International Journal of Approximate Rea- Soning, 44(1):, 4–31.eng
dcterms.referencesIshibuchi H. y Yamamoto T. (2004). Fuzzy rule selection by multi- objective genetic local search algorithms and rule evaluation mea- sures in data mining. Fuzzy Sets and Systems, (141(1):), 59–88.eng
dcterms.referencesJ., M. G. (1992). Discriminant analysis and statistical patern recognition (W. Interscience, Ed.).eng
dcterms.referencesJ. Rua, Z. R. (2014). ANÁLISIS DE MODELOS DE REDES NEURONALES ARTIFICIALES, PARA UN SISTEMA DE DIAGNÓSTICOS DE MIGRAÑAS CON AURA Y SIN AURA. Barranquilla – Colombia.spa
dcterms.referencesJ.R, Quinlan. (1993). C4.5: PROGRAMS FOR MACHINE LEARNING J. San Mateo, California.spa
dcterms.referencesJanikow, C.Z. (1993). A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 13, 189-228.eng
dcterms.referencesKalsoom, A., Maqsood, M., Ghazanfar, M. A., Aadil, F., & Rho, S. (2018). A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA). In Journal of Supercomputing (Vol. 74). https://doi.org/10.1007/s11227-018-2326-5eng
dcterms.referencesKangshun. Li, Yuanxiang. Li, Mo, H., & Chen, Z. (2005). A New Algorithm of Evolving Artificial Neural Networks via Gene Expression Programming. Journal of the Korean Society for Industrial and Applied Mathematics, 9(2), 83–90.eng
dcterms.referencesKearns.M., & Mansour. Y. (1995). On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. In Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, 459–468.eng
dcterms.referencesKennedy.Y:S:J:, & Eberhart. R. (2001). Swarrm Intelligence (Morgan. Kaufmann, Ed.).eng
dcterms.referencesKim, M., Hiroyasu, T., Miki, M., & Watanabe, S. (2010). SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. 742–751. https://doi.org/10.1007/978-3-540-30217-9_75eng
dcterms.referencesKohavi R. y John G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):, 273–324.eng
dcterms.referencesKonijn, R. M., Duivesteijn, W., Kowalczyk, W., & Knobbe, A. (2013). Discovering local subgroups, with an application to fraud detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7818 LNAI(PART 1), 1–12. https://doi.org/10.1007/978-3-642-37453-1_1eng
dcterms.referencesKoza J. R. (1992). Genetic programming as a means for program- ming computers by natural selection. Statistics and Computing. 4(2), 87–1.eng
dcterms.referencesKoza J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cam- Bridge MA, USA.eng
dcterms.referencesKumar, P. G., Kavitha, M. S., & Ahn, B. C. (2016). Automated detection of cancer associated genes using a combined fuzzy-rough-set-based f-information and water swirl algorithm of human gene expression data. PLoS ONE, 11(12), 1–24. https://doi.org/10.1371/journal.pone.0167504eng
dcterms.referencesLi, H., & Landa-Silva, D. (2008). Evolutionary multi-objective simulated annealing with adaptive and competitive search direction. 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 3311–3318. https://doi.org/10.1109/CEC.2008.4631246eng
dcterms.referencesLi R. P., M. M. y B. I. (2002). A fuzzy neural net- work for pattern classification and feature selection. Fuzzy Sets and Systems, 130(1):, 101–108.eng
dcterms.referencesLinfati, R., Escobar, J. W., & Gatica, G. (2014). Un algoritmo metaheurístico para el problema de localización y ruteo con flota heterogénea. Ingeniería y Ciencia, 10(19), 55–76. https://doi.org/10.17230/ingciencia.10.19.3spa
dcterms.referencesLiu H. y Yu L. (2005). Toward integrating feature selection al- gorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(3):, 491–502.eng
dcterms.referencesLobo, R. S., & Castro, A. M. (2011). Cromatico, un nuevo metodo de optimizacion que se fundamenta a través de un algoritmo de búsqueda basada en la escala cromática de las notas músicales. Universidad de Córdoba.spa
dcterms.referencesLu, Q., & Qiao, X. (2018). Sparse Fisher’s linear discriminant analysis for partially labeled data. Statistical Analysis and Data Mining, 11(1), 17–31. https://doi.org/10.1002/sam.11367eng
dcterms.referencesLuc, M., Marc, M., Ali, M., & Hammal, H. (2019). Rank correlated subgroup discovery ´.spa
dcterms.referencesMa, J., Guo, D., Liu, M., Ma, Y., & Chen, S. (2009). Rules extraction from ANN based on clustering. Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, CINC 2009, (2), 19–21. https://doi.org/10.1109/CINC.2009.168eng
dcterms.referencesMahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Mahdavi, S., Amani, M., & Granger, J. E. (2018). Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery. Remote Sensing of Environment, 206(January 2017), 300–317. https://doi.org/10.1016/j.rse.2017.11.005eng
dcterms.referencesMartí, R. (2003). Procedimientos Metahurísticos en Optimización Combinatoria. Valencia, Universidad de Valencia.spa
dcterms.referencesMartinez. R. (2014). Metodologías Basadas en Minería de Datos para el Diseño y Optimización de Técnicas de Clasificación Automática. Universidad de Murcia.spa
dcterms.referencesMelián, B., Moreno Pérez, J. A., Marcos, J., & Vega, M. (2003). Metaheuristics: A global view Metaheurísticas: una visión global. Inteligencia Artificial Revista Iberoamericana de Inteligencia Artificial., 19, 7–28. Retrieved from http://www.redalyc.org/pdf/925/92571901.pdfspa
dcterms.referencesMendes, R.R.F., Voznika, F.B., Freitas, A.A., and Nievola, J. C. (2001). Discovering fuzzy classification rules with genetic programming and co-evolution,. In Genetic and Evolutionary Computation Conference (GECCO-2001)., 183-194.eng
dcterms.referencesMendoza. M. (2016). ESCAPE STRATEGIES ALGORITHM (ESSA) UN NUEVO ALGORITMO META HEURÍSTICO DE OPTIMIZACIÓN GLOBAL PARA PROBLEMAS DE VARIABLE REAL, INSPIRADO EN LA INTERACCIÓN DEPREDADOR-PRESA. Universidad de Cordoba.spa
dcterms.referencesMerrikh-Bayat, F. (2015). The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Applied Soft Computing Journal, 33, 292–303. https://doi.org/10.1016/j.asoc.2015.04.048eng
dcterms.referencesMichalewicz Z. (1996). Genetic algorithms + data structures = evo- lution programs. Springer-Verlag.eng
dcterms.referencesMikut. R., Jäkel..J., & Gröll. L. (2005). Interpretability issues in data- based learning of fuzzy systems. Fuzzy Sets and Systems, 150(2):, 179–197.eng
dcterms.referencesMonteserin, A., & Armentano, M. G. (2018). Influence-based approach to market basket analysis. Information Systems, 78, 214–224. https://doi.org/10.1016/j.is.2018.01.008eng
dcterms.referencesMontgomery, D., Jennings, C., & Kulahci, M. (2008). Introduction to time series analysis and foecasting.eng
dcterms.referencesMorales. E, & Mariano. E. (1999). A Multiple Objective Ant-Q Algorithm for the Design of Water Distribution Irrigation Networks. Instituto Mexicano de Tecnolog__a Del Agua, (November 1999).eng
dcterms.referencesMori, Y., & Suzuki, T. (2018). Generalized ridge estimator and model selection criteria in multivariate linear regression. Journal of Multivariate Analysis, 165, 243–261. https://doi.org/10.1016/j.jmva.2017.12.006eng
dcterms.referencesMorillo Torres, D., Moreno, L., & Díaz, J. (2014). Metodologías Analíticas y Heurísticas para la Solución del Problema de Programaciónde Tareas con Recursos Restringidos (RCPSP): una revisión. Parte 2. Ingeniería y Ciencia, 10(20), 203–227. https://doi.org/10.17230/ingciencia.10.20.12spa
dcterms.referencesMotoda, H., Wu, X., Kumar, V., McLachlan, G. J., Zhou, Z.-H., Hand, D. J., … Ng, A. (2007). Top 10 algorithms in data mining. In Knowledge and Information Systems (Vol. 14). https://doi.org/10.1007/s10115-007-0114-2eng
dcterms.referencesNaghash Asadi, A., Abdollahi Azgomi, M., & Entezari-Maleki, R. (2019). Unified power and performance analysis of cloud computing infrastructure using stochastic reward nets. Computer Communications, 138(June 2018), 67–80. https://doi.org/10.1016/j.comcom.2019.03.004eng
dcterms.referencesNauck D. Kruse R. (1997). A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems, 89:, 277–288.eng
dcterms.referencesNaudin, M., Tremblais, B., Guillevin, C., Guillevin, R., & Fernandez, C. (2002). Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative Targeting Using Fuzzy Logic. Computers & Graphics, 26(3), 528. https://doi.org/10.1016/s0097-8493(02)00077-8eng
dcterms.referencesNieto, H. (2011). DISEÑO E IMPLEMENTACION DE UNA METAHEURISTICA HIBRIDA BASADA EN RECOCIDO SIMULADO, ALGORITMOS GENETICOS Y TEORIA DE AUTOMATAS PARA LA OPTIMIZACION BI-OBJETIVO DE PROBLEMAS COMBINATORIOS. Universidad del Norte, Barranquilla, Colombia.eng
dcterms.referencesNiño. (2012). Evolutionary Algorithms based on the Automata Theory for the Multi-objective Optimization of Combinatorial Problems. INT J COMPUT COMMUN, 7 (5)(1), 916–923. Retrieved from http://journal.univagora.ro/download/pdf/644.pdfeng
dcterms.referencesNiño., E., & Ardila., C. (2009). Algoritmo basado basado en la obtencion de optimos globales en problemas combnatorios. Ingenieria y Desarrollo, 25, 1–11.spa
dcterms.referencesNiño, E. (2010). Diseño e implementacion de una metaheuristica badasa en automatas finitos determistas para la optimizacion multiobjetivo de problemas Combinatorios. Universidad del Norte, Barranquilla, Colombia.spa
dcterms.referencesNiño, E. (2012). SAMODS and SAGAMODS: Novel Algorithms Based on the Automata Theory for the Multiobjective Optimization of Combinatorial Problems. International Journal of Artificial Intelligence, 8 S12. Retrieved from http://ceser.in/ceserp/index.php/ijai/article/view/1287eng
dcterms.referencesNiño, E., Ardila, C., Molinares, D., Barrios, A., & Yesid, D. (2011). MODS: A Novel Metaheuristic of Deterministic Swapping for the Multi-Objective Optimization of Combinatorials Problems. Computer Tecnology and Application, 2, 280–292.eng
dcterms.referencesNiño, E., Nieto, H., & Chinchilla, A. (2011). EMSA : Hybrid Metaheuristic based on Genetic Algorithms , Simulated Annealing and Deterministic Swapping. Universidad Del Norte, (4), 1–12.eng
dcterms.referencesNoda, E., Freitas, A.A., and Lopes, H. . (1999). Discovering Interesting Prediction Rules with a Genetic Algorithm. In IEEE Congress on Evolutionary Computation (CEC 99). Washington, USA.eng
dcterms.referencesNoda, E.; Freitas, A.A., and Lopes, H. S. (1999). Discovery Interesting Prediction Rules with a Genetic Algorithm. In IEEE Congress on Evolutionary Computation (CEC99)., Washington. USA.eng
dcterms.referencesOberlin.P, Rathinam.S, & Darbha.S. (2009). A transformation for a Multiple Depot, Multiple Traveling Salesman Problem. American Control Conference, 2636–2641.eng
dcterms.referencesOlivas, F., Valdez, F., Melin, P., Sombra, A., & Castillo, O. (2019). Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. Information Sciences, 476, 159–175. https://doi.org/10.1016/j.ins.2018.10.025eng
dcterms.referencesPapadakis S. E. y Theocharis J. B. (2006). A genetic method for designing TSK models based on objective weighting: application to classification problems. Soft Computing, 10(9):, 805–824.eng
dcterms.referencesParthasarathy, S., Zaki, M. J., Ogihara, M., & Li, W. (1997). New Algorithms for Fast Discovery of Association. 283–286.eng
dcterms.referencesPearl J. (1988). Probabilistic reasoning in intelligent systems. Mor- gan Kaufmann, Palo Alto, USA. In Mor- gan Kaufmann, Palo Alto, USA.eng
dcterms.referencesPérez-Ortega, J., Castillo-Zacatelco, H., Vilariño-Ayala, D., Mexicano-Santoyo, A., Zavala-Díaz, J. C., Martínez-Rebollar, A., & Estrada-Esquivel, H. (2016). Una nueva estrategia heurística para el problema de Bin Packing. Ingeniería, Investigación y Tecnología, 17(2), 155–168. https://doi.org/10.1016/j.riit.2016.06.001spa
dcterms.referencesPérez. E, Herrera. F., & Hernández. C. (2003). Finding multiple solu- tions in job shop scheduling by niching genetic algorithms. Journal of Intelligent Manufacturing, 14(3-4):, 323–339.eng
dcterms.referencesPérez. M, Pérez, . P, Rivera. A, Jesus. M, & López. P. (2010). CO 2 RBFN : predicción de series temporales con un enfoque cooperativo-competitivo. Soft Computing 14, 14, 953–971. Retrieved from https://sci2s.ugr.es/keel/pdf/keel/congreso/maeb09.pdfspa
dcterms.referencesPham, B. T., & Prakash, I. (2019). Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping. Geocarto International, 34(3), 316–333. https://doi.org/10.1080/10106049.2017.1404141eng
dcterms.referencesProvost, F., & Fawcett, T. (2001). Robust classification for imprecise environments. Machine Learning, 42(3), 203–231. https://doi.org/10.1023/A:1007601015854eng
dcterms.referencesQuinlan. J. R. (1986). Induction of Decision Trees. In Machine Learning, 1, 81-106.eng
dcterms.referencesQuinlan. J. R. (1993). C4.5: Programs for Machine Learning. In Morgan Kaufmann Publishers.eng
dcterms.referencesRamírez. J, Osuna. I, Rojas. J, & Guerrero. S. (2016). Remuestreo Bootstrap y Jackknife en confiabilidad: Caso Exponencial y Weibull. Revista Facultad De Ingeniería, 25(41), 55. https://doi.org/10.19053/01211129.4137spa
dcterms.referencesRanjan, R., & Sharma, A. (2019). Evaluation of Frequent Itemset Mining Platforms using Apriori and FP- Growth Algorithm. Department of Computer Science and Engineering, DIT University, (February), 1–6.eng
dcterms.referencesRavi V., R. P. J. y Z. H. J. (2000). Pattern clas- sification with principal component analysis and fuzzy rule bases. European Journal of Operational Research, 126(3):, 526–533.eng
dcterms.referencesRavi V. y Zimmermann H. J. (2001). A neural network and fuzzy rule base hybrid for pattern classification. Soft Computing, 5(2):, 152–159.eng
dcterms.referencesRiaño. J., Prieto. F., Sánchez. E, C, A., & Castellano. G. (2010). Analysis and convergence of weighted dimensionality reduction methods. Revista Facultad De Ingeniería. Universidad de Antioquia, 1–11.eng
dcterms.referencesRocha, L.; González, C. y Orjuela, J. (2011). Una revisión al estado del arte del problema de ruteo de vehículos: Evolución histórica y métodos de solución. Ingeniería, Vol. 16, N, 35-55 .spa
dcterms.referencesRodríguez.C, Pena. M, & Piñero. P. (2015). Aprendizaje de reglas difusas usando algoritmos genéticos. Congreso Internacional de Matemática y Computación COMPUMAT, At La Habana, Cuba.spa
dcterms.referencesRomao, W., Freitas, A.A., and Pacheco, R. C. S. (2002a). A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data. In Genetic and Evolutionary Computation Conference (GECCO.eng
dcterms.referencesRomao, W., Freitas, A.A., and Pacheco, R. C. S. (2002b). A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data. In Genetic and Evolutionary Computation Conference (GECCO 2002).eng
dcterms.referencesRomero, G. P., & Niz, A. P. (2013). facultad Ingeniería de Sistemas , con técnicas de minería de datos. Https://Revistas.Unisimon.Edu.Co, 1–6. Retrieved from https://revistas.unisimon.edu.co/index.php/identic/article/view/2484spa
dcterms.referencesRoubos J. A., S. M. y A. J. (2003). Learning fuzzy clas- sification rules from labeled data. Information Sciences, 150(1-2):, 77–93.eng
dcterms.referencesRuiz, J. (2013). ENTRENAMIENTO DE REDES NEURONALES ARTIFICIALES BASADO EN ALGORITMO EVOLUTIVO Y TEORÍA DE AUTÓMATAS FINITOS. tesis de maestia sin publicacion. Universidad del Norte, Barranquilla, Colombia.spa
dcterms.referencesS. Garcia., J .Derrac., F. Herrera., & D. Molina. (2012). Un tutorial sobre el uso de test estadısticos no parametricos en comparaciones multiples de metaheurısticas y algoritmos evolutivos. 8–10.spa
dcterms.referencesSampieri, R. H., Collado, C. F., & Baptista, M. del pilar. (2010). METODOLOGIA DE LA INVESTIGACIÓN (QUINTA EDI). Mc Graw Hill.spa
dcterms.referencesSchwefel H. P. (1995). Evolution and Optimum Seeding. Wiley Inc.eng
dcterms.referencesSetnes M. Roubos H. (2000). GA-fuzzy modeling and classifica- tion: complexity and performance. IEEE Transactions on Fuzzy Systems, 8(5):, 509–522.eng
dcterms.referencesSetnes M. y Babuška R. (2001). Rule base reduction: Some com- ments on the use of orthogonal transforms. IEEE Transactions on Systems, Man, and Cybernetics, Part B 31:, 199–206.eng
dcterms.referencesSeymour, L., Brockwell, P. J., & Davis, R. A. (2006). Introduction to Time Series and Forecasting. In Journal of the American Statistical Association (Vol. 92). https://doi.org/10.2307/2965440eng
dcterms.referencesShen Q. Jensen R. (2004). Selecting informative features with fuzzy-rough sets and its application for complex systems monitor ing. Pattern Recognition, 37(7):, 1351–1363.eng
dcterms.referencesSheskin, D. J. (2000). PARAMETRIC and NONPARAMETRIC STATISTICAL PROCEDURES SECOND EDITION. Retrieved from www.crcpress.comeng
dcterms.referencesSilipo R. y Berthold M. R. (2000). Input features’ impact on fuzzy decision processes. IEEE Transactions on Systems, Man, and Cy- Bernetics, Part B 30(, 821–834.eng
dcterms.referencesSimon, C., Weber, P., & Evsukoff, A. (2008). Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis. Reliability Engineering and System Safety, 93(7), 950–963. https://doi.org/10.1016/j.ress.2007.03.012eng
dcterms.referencesStreet, W. (1982). SOME PROBLEMS WITH FROM FINITE MIXTURE DISTRIBUTIONS. Contract.eng
dcterms.referencesSui. X, & Leung. H. (2008). An Adaptive Bidding Strategy in Multi-round Combinatorial Auctions for Resource Allocation," Tools with Artificial Intelligence. ICTAI ’08. 20th IEEE International Conference, 2, 423–430. https://doi.org/978-0-7695-3440-4eng
dcterms.referencesTovar, L., Coronell, M., & Donoso, Y. (2007). Optimización multiobjetivo en redes ópticas con transmisión Multicast, utilizando algoritmos evolutivos y lógica difusa. Ingeniería & Desarrollo, 21.spa
dcterms.referencesTsakiridis, N. L., Theocharis, J. B., & Zalidis, G. C. (2016). DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems. Knowledge-Based Systems, 105, 160–174. https://doi.org/10.1016/j.knosys.2016.05.013eng
dcterms.referencesUlungu, E. L., Teghem, J., Fortemps, P. H., & Tuyttens, D. (1999). MOSA method: A tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8(4), 221–236. https://doi.org/10.1002/(SICI)1099-1360(199907)8:4<221::AID-MCDA247>3.0.CO;2-Oeng
dcterms.referencesVaezpour, E., Dehghan, M., & Yousefi’zadeh, H. (2019). Robust joint user association and resource partitioning in heterogeneous cloud RANs with dual connectivity. Computer Communications, 138, 1–10. https://doi.org/10.1016/j.comcom.2019.02.008eng
dcterms.referencesVan Broekhoven E., A. V. y D. B. B. (2007). Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study. International Journal of Approximate Reasoning, 44(1):, 65– 90.eng
dcterms.referencesVidya, V., & Nedunchezhian, R. (2011). A robust weighted association rule mining using FP-tree. European Journal of Scientific Research, 66(4), 600–609.eng
dcterms.referencesWang J.-S. Lee C. S. G. (2000). Self-adaptive neuro-fuzzy infer- ence systems for classification applications. IEEE Transactions on Fuzzy Systems, 10(6):, 790–802.eng
dcterms.referencesWeise, T. (2008). Global Optimization Algorithms Theory and Application (segunda ed).eng
dcterms.referencesWeiss, S. I., & Kulikowski, C. (1991). Computer System Than Learn: Classification y Prediction Methods from Statistic, Neural Networks, Machine Learning, and Expert System. San francisco, California: Morgan Kaufmann.eng
dcterms.referencesWhitley L. D. y Kauth J. (1988). GENITOR: A different genetic algorithm. En Proceedings of the Rocky Mountain Conference on Artificial Intelligence, páginas 118–130. Denver, USA.eng
dcterms.referencesWitten, I. H., & Frank, E. (2000). Machine Learning Algorithms in Java Nuts and bolts. 58. Retrieved from https://www.cs.ru.nl/P.Lucas/teaching/DM/weka.pdfeng
dcterms.referencesY. F. Cabrera. (2011). MÉTODOS DE APRENDIZAJE PARA DOMINIOS CON DATOS MEZCLADOS BASADOS EN LA TEORÍA DE LOS CONJUNTOS APROXIMADOS EXTENDIDA. Universidad Central Marta Abreu.spa
dcterms.referencesYañez. E. (2009). T ecnicas de Auto-Adaptacion para Algoritmos Evolutivos Multi-objetivo. CENTRO DE INVESTIGACION Y DE ESTUDIOS AVANZADOS DEL INSTITUTO POLITECNICO NACIONAL DEPARTAMENTO DE COMPUTACION.spa
dcterms.referencesYen J. Wang L. (1999). Simplifying fuzzy rule-based models us- ing orthogonal transformation methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B 29:, 13–24.eng
dcterms.referencesYu S., B. S. D. y S. P. (2002). Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hy- perspectral satellite imagery. Pattern Recognition Letters, 23(1-3):, 183–190.eng
dcterms.referencesZadeh L. A. (1975). The concept of a linguistic variable and its applications to approximate reasoning, parts I, II, III. Information Sciences, 199–249, 301–357, 43–80.eng
dcterms.referencesZadeh, L.A. (1965). fuzzy sets. Journal of Plant Pathology, 338–353.eng
dcterms.referencesZadeh, L.A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 28-44. https://doi.org/https://doi.org/10.1109/TSMC.1973.5408575eng
dcterms.referencesZadeh, Lotfi A. (1994). Soft Computing and Fuzzy Logic. IEEE Software, 11(6), 48–56. https://doi.org/10.1109/52.329401eng
dcterms.referencesZhang, J., Wang, Y., & Feng, J. (2013). Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm. The Scientific World Journal, 2013. https://doi.org/10.1155/2013/259347eng
dcterms.referencesZhang, X., Wang, C., & Wu, Y. (2018). Functional envelope for model-free sufficient dimension reduction. Journal of Multivariate Analysis, 163, 37–50. https://doi.org/10.1016/j.jmva.2017.09.010eng
dcterms.referencesZitzler, E.; Thiele, L.; Laumanns, M.; Fonseca, C.: da Fonseca, V. (2003). Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation, Vol 7, No, pag. 117–132,.eng
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