COmbining multiple neural networks for classification based on rough set reduction

Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03

Volumn 1, Issue , 2003, Pages 543-548

  Yu, Daren ^a   Hu, Qinghua ^a   Bao, Wen ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINED SYSTEM; CONCISE REPRESENTATIONS; GENERALIZATION ABILITY; HIGH DIMENSIONAL DATA; MAJORITY VOTING; MEASURE OF PERFORMANCE; MULTIPLE NEURAL NETWORKS; RECOGNITION PERFORMANCE; REDUCTS; ROUGH SET REDUCTION; SINGLE SAMPLE;

CLASSIFIERS; CLUSTERING ALGORITHMS; PATTERN RECOGNITION; ROUGH SET THEORY; SIGNAL PROCESSING;

NEURAL NETWORKS;

EID: 33646014305     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNNSP.2003.1279331     Document Type: Conference Paper

References (20)

1. Sarker D. "Randomness in generalization ability: A source to improve it". IEEE Trans. Neural networks, 7: 676-685, 1996
2. S.B. Cho. "Pattern recognition with neural networks combined by genetic algorithm", Fuzzy sets and systems 103: 339-347, 1999
3. T. M. Ha, H. Bunke, "Off-Line handwritten numeral recognition by perturbation method", IEEE Trans. Pattern Anal. Mach. Intell. 19(5): 535-539, 1997
4. G. Rogova, "Combining the result of several neural network classifiers", Neural Network (5): 777-781, 1994
5. T. K. Ho, Random decision forests, Third Int. Conf. Document Analysis and recognition, pp.278-282, Montreal, 14-16 Aug. 1995
6. J. Cao, M. Ahmadi, M, Shridhar, "Recognition of handwritten numerals with multiple feature and multistage classifier", Pattern recognition, vol.28, no. 2: 153-160, 1995
7. F. Kimural, M. Shridhar, "handwritten Numerical recognition based on multiple algorithm", pattern recognition, vol. 24, no. 10: 969-983, 1991
8. P.N. Suganthan, "Pattern classification using multiple hierarchical overlapped self-organising maps", Pattern recognition 34:2173-2179, 2001
9. Sherif Hashem and Bruce Schmeiser, "Improving model Accuracy using optimal linear combinations of trained neural networks", IEEE Trans. Neural Networks, vol. 6, no. 3: 792-794, 1995
10. J. Kittler, M. Hatef, R. P.W. Duin, J. Matas, "On combining classifiers", IEEE Trans. Pattern Anal. and Mach. Intell., vol. 20, no.3:226-239, 1998
11. G Giancinto, F. Roli, "Adaptive selection of image classifiers", proceedings of the 9th. ICIAP, Lecture Notes in computer science, vol.1310, Springer, Berlin, 1997, pp.38-45
12. Z. Pawlak, "Rough set", Int. J. of computer and information science, vol. 11 1982:341-356
13. Pawlak, Z., "Rough set theory for intelligent industrial applications", IPMM '99, pp: 37-44, Jul 1999
14. J. Wang, D.Q. Miao, "Analysis on attribute reduction strategies of rough set", J. computer Sci. & Tech. vol.13, No.2: 189-193, 1998
15. Jue Wang, Ju Wang. "Reduction algorithms based on discernibility matrix: the ordered attributes method". J. Computer Sci. & Tech. vol.16, No.6:489-504, 2001
16. R. Bautista, M. Millan, J. F. Diaz, "An efficient implementation to calculate relative core and reducts", IEEE: 791-794, 1999
17. Ann, B.S.; Cho, S.S.; Kim, C.Y. "The integrated methodology of rough set theory and artificial neural network for business failure prediction", Expert Systems with Applications, Vol.18, Issue: 2, pp. 65-74, 2000
18. H. K. Wei, S. X. Xu, W. Z. Song, "Generalization theory and generalization methods for neural networks", Automatica SINICA, Vol.27, No. 6: 806 - 815, 20021
19. R. W. Swiniarski, A. Skowron, "Rough set methods in feature selection and recognition". Pattern recognition letters, vol. 24, 833-849, 2003
20. Shen, Qiang; Chouchoulas, Alexios, "A rough-fuzzy approach for generating classification rules", Pattern Recognition, Vol.: 35, Issue: 11, pp. 2425-2438, 2002