Multigene Genetic Programming Based Fuzzy Regression for Modelling Customer Satisfaction Based on Online Reviews

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yakubu:2019:IEEM,

• author = "H. Yakubu and C. K. Kwong",
• booktitle = "2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)",
• title = "Multigene Genetic Programming Based Fuzzy Regression for Modelling Customer Satisfaction Based on Online Reviews",
• year = "2019",
• pages = "1541--1545",
• month = dec,
• keywords = "genetic algorithms, genetic programming, multigene genetic programming, customer satisfaction models, fuzzy regression, online reviews",
• ISSN = "2157-362X",
• DOI = "doi:10.1109/IEEM44572.2019.8978852",
• abstract = "As markets become increasingly competitive, most businesses have adopted modern practices that helps them to enhance the competitiveness of their products. Such practices involve the use of internet though which companies gain insights into the concerns of their customers. For instance, the proliferation of e-commerce websites has enabled consumers to voice their opinions on the products they have purchased. This study proposes a methodology for modeling customer satisfaction (CS) based on online reviews using a new multigene genetic programming based fuzzy regression (MGGP-FR). Polynomial structures of CS models were developed by employing the multigene genetic programming method. The fuzzy coefficients of the polynomial structures were then determined using the fuzzy regression analysis. The proposed method was illustrated using an electronic hair dryer as a case study. The validation test results indicated that MGGP-FR the outperformed the genetic programming based fuzzy regression and the fuzzy regression analysis in terms of prediction errors.",
• notes = "Also known as \cite{8978852}",

}

Genetic Programming entries for Hanan Yakubu Che Kit Kwong

Citations