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

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...

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Veröffentlicht in:Neural computing & applications Jg. 22; H. 3-4; S. 783 - 791
1. Verfasser: Lin, Su-Mei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer-Verlag 01.03.2013
Springer
Schlagworte:
Applied sciences
Artificial Intelligence
Calculus of variations and optimal control
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Drosophila
Exact sciences and technology
Formicidae
Image Processing and Computer Vision
Learning and adaptive systems
Linear inference, regression
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Original Article
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
Principal component regression
Service satisfaction
General regression neural network
Data mining
Fruit fly optimization algorithm
OR algorithm
Data analysis
Neural computation
Questionnaire
Auction
Optimization method
Prediction theory
Neural network
Dependent variable
Independent variable
Optimization
Statistical regression
Genetic algorithm
Capacity
Classification
Detection
Sample survey
Service quality
Principal component analysis
ISSN:0941-0643, 1433-3058
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Zusammenfassung: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 (PSOGRNN), 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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-011-0769-1