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TSK-0 Fuzzy Rule-Based Systems for High-Dimensional Problems Using the Apriori Principle for Rule Generation.

, , and . RSCTC, volume 8536 of Lecture Notes in Computer Science, page 270-279. Springer, (2014)

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An Application of Dynamic Bayesian Networks to Condition Monitoring and Fault Prediction in a Sensored System: a Case Study., , and . Int. J. Comput. Intell. Syst., 10 (1): 176-195 (2017)Learning TSK-0 linguistic fuzzy rules by means of local search algorithms., , and . Appl. Soft Comput., (2014)CiDAEN: An Online Data Science Course., , , , , , , , , and 1 other author(s). HELMeTO, volume 1091 of Communications in Computer and Information Science, page 113-124. Springer, (2019)A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity., , , and . IEEE Trans. Fuzzy Syst., 27 (4): 701-715 (2019)Learning heterogeneus cooperative linguistic fuzzy rules using local search: Enhancing the COR search space., , and . ISDA, page 475-480. IEEE, (2011)InferPy: Probabilistic modeling with deep neural networks made easy., , , and . Neurocomputing, (2020)Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings., , , and . IFSA-EUSFLAT, Atlantis Press, (2015)Generation of first-order TSK rules based on the apriori + search approach., , and . CEC, page 1675-1682. IEEE, (2017)Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach., , and . Inf. Sci., (2018)Probabilistic Graphical Models with Neural Networks in InferPy., , , and . PGM, volume 138 of Proceedings of Machine Learning Research, page 601-604. PMLR, (2020)