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

Neural Computing and Applications

Volumn 22, Issue 3-4, 2013, Pages 783-791

  Lin, Su Mei ^a  

^a CHINA UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Data mining; Fruit fly optimization algorithm; General regression neural network; Principal component regression; Service satisfaction

Indexed keywords

ANT COLONY OPTIMIZATION; DATA MINING; FRUITS; GENETIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); PREDICTIVE ANALYTICS; REGRESSION ANALYSIS; SURVEYS; WEB SERVICES;

ANT COLONY OPTIMIZATION ALGORITHMS; FRUIT FLIES; FRUIT FLY OPTIMIZATION ALGORITHM (FOA); GENERAL REGRESSION NEURAL NETWORK; PARTICLE SWARM OPTIMIZATION ALGORITHM; PRINCIPAL COMPONENT REGRESSION; PRINCIPAL COMPONENT REGRESSION ANALYSIS; SERVICE SATISFACTION;

NEURAL NETWORKS;

EID: 84874019736     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0769-1     Document Type: Article

References (17)

1. Bakos JY (1997) Reducing buyer search costs: implications for electronic marketplaces. Manag Sci 43(12): 1676-1692.
2. Aldridge A, Forcht K, Pierson J (1997) Get linked or get lost: marketing strategy for the internet. Internet Res 7: 161-169.
3. Kim KJ, Han I (2000) Genetic algorithms approach to feature discrimination in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19: 125-132.
4. Paul RJ, Chanev TS (1997) Optimising a complex discrete event simulation model using a genetic algorithm. Neural Comput Appl 6(4): 229-237.
5. Wen JS (2009) PSO-based adaptive artificial neural networks for wind speed forecasting. National Cheng Kung University Master's Thesis.
6. Han F, Ling QH, Huang DS (2010) An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks. Neural Comput Appl 19(2): 255-261.
7. Su CT, Wong JT, Tsou SC (2005) A process parameters determination model by integrating artificial neural network and ant colony optimization. J Chin Inst Indus Eng 22(4): 346-354.
8. Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16(3): 235-247.
9. Pan WT (2011) A new evolutionary computation approach: fruit fly optimization algorithm. In: 2011 conference of digital technology and innovation management, Taipei. Program code on the website: http://www. oitecshop. byethost16. com/FOA. html.
10. Pan WT (2011) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26: 69-74.
11. Pan WT (2011) Fruit fly optimization algorithm. Tsang Hai Book Publishing Co., Taipei.
12. Specht DF (1990) Probabilistic neural networks and the polynomial adaline as complementary techniques for classification. IEEE Trans Neural Netw 1(1): 111-121.
13. Eberhart RC, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of sixth international symposium on Nagoya, Japan, pp 39-43.
14. Yeh YC (2001) The model application and practice of artificial neural network. Scholars Books Co., Ltd.
15. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6): 568-576.
16. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Patt Recognit 30(7): 1145-1159.
17. Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve to multiple class classification problems. Mach Learn 45(2): 171-186.