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Fuzzy Classification Rules with FRvarPSO Using Various Methods for Obtaining Fuzzy Sets

Patricia Jimbo Santana 1, Laura Lanzarini 2, and Aurelio F, Bariviera 3
1. Accounting and Auditing Branch, School of Administration Science, Central University of Ecuador, Quito, Ecuador
2. Computer Science Research Institute LIDI (III-LIDI), School of Computer Science, National University of La Plata, La Plata, Buenos Aires, Argentina
3. Department of Business, Rovira i Virgili University, Avenida de la Universitat, 1 Reus, Spain

Abstract—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.
 
Index Terms—FRvarPSO (Fuzzy Rules variable Particle Swarm Optimization), fuzzy rules, classification rules, fuzzy C-means, data mining

Cite: Patricia Jimbo Santana, Laura Lanzarini, and Aurelio F, Bariviera, "Fuzzy Classification Rules with FRvarPSO Using Various Methods for Obtaining Fuzzy Sets," Journal of Advances in Information Technology, Vol. 11, No. 4, pp. 233-240, November 2020. doi: 10.12720/jait.11.4.233-240

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.