Reasoning Methods in Fuzzy Rule-based Classification Systems for Big Data Problems
González Muñoz, Antonio
Pérez Rodríguez, Francisco G.Raúl
Romero Zaliz, Rocio Celeste
Approximate Reasoning
Fuzzy Rules
Classifications Problems
Big Data
The analysis with a very high number of examples is a subject of growing interest that needs new algorithms
and procedures. In this case, we study how the massive use of data affects the reasoning processes for classification
problems that make use of fuzzy rule-based systems. First, we describe the standard reasoning model
and the operations associated with its use, and once it is verified that these calculations may be inefficient in
some cases we propose a new model to perform such calculations. Basically, the proposal eliminates the need
to review all the rules in every inference process, generating the rule that best adapts to the particular example,
which does not have to be part of the set of rules, and from it explore only the rules that have some effect on
the example. We make an experimental study that shows the interest of the proposal presented.
2020-12-02T11:02:14Z
2020-12-02T11:02:14Z
2019-05-04
info:eu-repo/semantics/conferenceObject
González, A., Pérez, R., & Romero-Zaliz, R. (2019). Reasoning Methods in Fuzzy Rule-based Classification Systems for Big Data Problems. In IoTBDS (pp. 255-261). [DOI:10.5220/0007709002550261]
http://hdl.handle.net/10481/64589
10.5220/0007709002550261
eng
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
Atribución-NoComercial-SinDerivadas 3.0 España
ScitePress