Treffer: Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network

Title:
Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network
Authors:
LIN, Su-Mei
Source:
Neural computing & applications (Print). 22(3-4):783-791
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence linéaire, régression, Linear inference, regression, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse donnée, Data analysis, Análisis datos, Calcul neuronal, Neural computation, computación neuronal, Capacité, Capacity, Capacidad, Classification, Clasificación, Détection, Detection, Detección, Fouille donnée, Data mining, Busca dato, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Qualité service, Service quality, Calidad servicio, Questionnaire, Cuestionario, Régression statistique, Statistical regression, Regresión estadística, Réseau neuronal, Neural network, Red neuronal, Sondage statistique, Sample survey, Ecuesta estadística, Théorie prédiction, Prediction theory, Variable indépendante, Independent variable, Variable independiente, 49XX, 62H25, 62H30, 62Jxx, 62M45, 65K10, 65Kxx, Algorithme QR, OR algorithm, Enchère, Auction, Modèle réseau neuronal, Réseau neuronal artificiel, Variable dépendante, Dependent variable, Fruit fly optimization algorithm, General regression neural network, Principal component regression, Service satisfaction
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Marketing and Logistics, China University of Technology, 530, Chung-San Rd, Sec. 3, Hu-Kuo Township, Hsin-Chu, Tawain, Province of China
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27659255
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.27659255
Database:
PASCAL Archive

Weitere Informationen

When constructing classification and prediction models, most researchers used genetic algorithm, particle swarm optimization algorithm, or ant colony optimization algorithm to optimize parameters of artificial neural network models in their previous studies. In this paper, a brand new approach using Fruit fly optimization algorithm (FOA) is adopted to optimize artificial neural network model. First, we carried out principal component regression on the results data of a questionnaire survey on logistics quality and service satisfaction of online auction sellers to construct our logistics quality and service satisfaction detection model. Relevant principal components in the principal component regression analysis results were selected for independent variables, and overall satisfaction level toward auction sellers' logistics service as indicated in the questionnaire survey was selected as a dependent variable for sample data of this study. In the end, FOA-optimized general regression neural network (FOAGRNN), PSO-optimized general regression neural network (PSO-GRNN), and other data mining techniques for ordinary general regression neural network were used to construct a logistics quality and service satisfaction detection model. In the study, 4-6 principal components in principal component regression analysis were selected as independent variables of the model. Analysis results of the study show that of the four data mining techniques, FOA-optimized GRNN model has the best detection capacity.