Autor según el artículo: Santana PJ; Lanzarini L; Bariviera AF

Departamento: Gestió d'Empreses

Autor/es de la URV: Fernández Bariviera, Aurelio

Palabras clave: Index terms—frvarpso (fuzzy rules variable particle swarm optimization) Fuzzy rules Fuzzy c-means Data mining Classification rules

Resumen: © 2020 J. Adv. Inf. Technol. Having strategies capable of automatically generating classification rules is highly useful in any decision-making process. In this article, we propose a method that can operate on nominal and numeric attributes to obtain fuzzy classification rules by combining a competitive neural network with an optimization technique based on variable population particle swarms. The fitness function that controls swarm movement uses a voting criterion that weights, in a fuzzy manner, numeric attribute participation. The efficiency and efficacy of this method are strongly conditioned by how membership functions to each of the fuzzy sets are established. In previous works, this was done by partitioning the range of each numeric attribute at equal-length intervals, centering a triangular function with appropriate overlap in each of them. In this case, an improvement to the fuzzy set generation process is proposed using the Fuzzy C-Means methods. The results obtained were compared to those yielded by the previous version using 11 databases from the UCI repository and three databases from the Ecuadorian financial system – one from a credit and savings cooperative and two from banks that grant productive and non-productive credits as well as microcredits. The results obtained were satisfactory. At the end of the article, our conclusions are discussed and future research lines are suggested.

Áreas temáticas: Software Information systems Computer science, interdisciplinary applications Computer science, information systems Computer science applications Computer networks and communications Artificial intelligence

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: aurelio.fernandez@urv.cat

Identificador del autor: 0000-0003-1014-1010

Fecha de alta del registro: 2023-08-05

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: http://www.jait.us/index.php?m=content&c=index&a=show&catid=203&id=1123

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Journal Of Advances In Information Technology. 11 (4): 233-240

Referencia de l'ítem segons les normes APA: Santana PJ; Lanzarini L; Bariviera AF (2020). Fuzzy classification rules with frvarpso using various methods for obtaining fuzzy sets. Journal Of Advances In Information Technology, 11(4), 233-240. DOI: 10.12720/jait.11.4.233-240

DOI del artículo: 10.12720/jait.11.4.233-240

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications