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Fuzzy classification rules with frvarpso using various methods for obtaining fuzzy sets

  • Identification data

    Identifier: imarina:9002813
    Authors:
    Santana PJLanzarini LBariviera AF
    Abstract:
    © 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.
  • Others:

    Author, as appears in the article.: Santana PJ; Lanzarini L; Bariviera AF
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Index terms—frvarpso (fuzzy rules variable particle swarm optimization) Fuzzy rules Fuzzy c-means Data mining Classification rules
    Abstract: © 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.
    Thematic Areas: Software Information systems Computer science, interdisciplinary applications Computer science, information systems Computer science applications Computer networks and communications Artificial intelligence
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: aurelio.fernandez@urv.cat
    Author identifier: 0000-0003-1014-1010
    Record's date: 2023-08-05
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: http://www.jait.us/index.php?m=content&c=index&a=show&catid=203&id=1123
    Papper original source: Journal Of Advances In Information Technology. 11 (4): 233-240
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.12720/jait.11.4.233-240
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Science, Information Systems,Computer Science, Interdisciplinary Applications,Information Systems,Software
    Index terms—frvarpso (fuzzy rules variable particle swarm optimization)
    Fuzzy rules
    Fuzzy c-means
    Data mining
    Classification rules
    Software
    Information systems
    Computer science, interdisciplinary applications
    Computer science, information systems
    Computer science applications
    Computer networks and communications
    Artificial intelligence
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