Fuzzy systems for data analysis
Models that use fuzzy sets as a tool in data analysis allow knowledge to be represented in a way that is very close to natural language. Additionally, fuzzy systems allow modeling complex scenarios such as those with lack of data or uncertainty.
Fuzzy evolutionary systems, which are a prominent area in Computational Intelligence, are evolutionary algorithms applied to the design of fuzzy systems. This hybridization provides additional skills to fuzzy modeling in different Data Science scenarios.
Today, the core of data analysis does not is only intended to be as accurate as possible, but also to make it explainable. In this sense, data analysis methods based on fuzzy systems and fuzzy evolutionary systems maintain the original essence of comprehensibility established by Zadeh by increasing its data analysis skills. This provides advantages over other paradigms for obtaining XAI learning models, including transparency, comprehensibility, and understandability.
Among the current lines of work in fuzzy systems are the optimization of components in fuzzy systems, fuzzy modeling in emerging scenarios in data analysis as unique or non-standard problems, big data or proposals in the field of Explained Artificial Intelligence.
Contact: Antonio Gonzalez Muñoz
Related Researchers:
Letra: |
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| Name | Area | Cat. | ||
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Alcalá Fernández, Rafael | alcala@decsm4YIURmZDiZSai.ugr.es | Data Science and Big Data Area | PhD |
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Casillas Barranquero, Jorge | casillas@decsaiumYBCv08eDx.ugr.es | Data Science and Big Data Area | PhD |
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Gacto Colorado, María José | mgacto@uj9U31ermeu2TUaen.es | Data Science and Big Data Area | PhD |
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Giráldez Crú, Jesús | jgiraldez@ugrRbBx4PI2J9AX.es | DaSCI Technology Applications Area | PhD - Juan de la Cierva |
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Morente Molinera, Juan Antonio | jamoren@6@O0ORZdugr.es | DaSCI Technology Applications Area | PhD - Juan de la Cierva |
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Peregrin Rubio, Antonio | peregrin@dti.uhu.Rhjo4fes | Computational Intelligence Area | PhD |