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:


  Name Email Area Cat.
Alcalá Fernández, Rafael alcala@decsm4YIURmZDiZSai.ugr.es Data Science and Big Data Area PhD
Casillas Barranquero, Jorge casillas@decsaiumYBCv08eDx.ugr.es Data Science and Big Data Area PhD
Gacto Colorado, María José mgacto@uj9U31ermeu2TUaen.es Data Science and Big Data Area PhD
Giráldez Crú, Jesús jgiraldez@ugrRbBx4PI2J9AX.es DaSCI Technology Applications Area PhD - Juan de la Cierva
Morente Molinera, Juan Antonio jamoren@6@O0ORZdugr.es DaSCI Technology Applications Area PhD - Juan de la Cierva
Peregrin Rubio, Antonio peregrin@dti.uhu.Rhjo4fes Computational Intelligence Area PhD
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