Multistage classifiers optimized by neural networks and genetic algorithms

Nonlinear Analysis, Theory, Methods and Applications

Volumn 30, Issue 3, 1997, Pages 1323-1334

  Benediktsson, Jon Atli ^a   Sveinsson, Johannes R ^a   Ingimundarson, Jon Ingi ^a   Sigurdsson, Helgi Steinar ^a   Ersoy, Okan K ^b  

^a UNIVERSITY OF ICELAND   (Iceland)

^b PURDUE UNIVERSITY   (United States)

Author keywords

Combination of classifiers; Consensus theory; Data fusion; Fuzzy logic; Genetic algorithms; Neural networks; Optimization

Indexed keywords

EID: 0042632506     PISSN: 0362546X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0362-546X(97)00222-8     Document Type: Article

References (22)

1. WOLPERT D. H., Stacked generalization, Neural Networks 5, 241-259 (1992).
2. TUMER K. & GHOSH J., Theoretical foundations of linear and order statistics combiners for neural pattern classifiers, to appear IEEE Transactions on Neural Networks.
3. HANSEN L. K. & SALAMON P., Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993-1001 (1990).
4. ALPAYDIN E., Multiple networks for function learning, Proceedings of the 1993 IEEE International Conference on Neural Networks 1, 9-14, San Francisco (1993).
5. BATTITI R. & COLLA A. M., Democracy in neural nets: Voting schemes for classification, Neural Networks 7, 691-707 (1994).
6. ROGOVA G., Combining the results of several neural network classifiers, Neural Networks 7, 777-781 (1994).
7. CHO S-B & KIM J. H., Multiple network fusion using fuzzy logic, IEEE Transactions on Neural Networks 6, 497-501 (1995).
8. PERRONE M. P. & COOPER L. N., When networks disagree: Ensemble methods for hybrid neural networks, Neural Networks for Speech and Image Processing (Edited by R.J. MAMMONE), Champman-Hill, London (1993).
9. JACOBS R. A., JORDAN M. I., NOWLAN S. J. & HINTON G. E., Adaptive mixtures of local experts, Neural Computation 3, 79-87 (1991).
10. BENEDIKTSSON J. A. & SWAIN P. H., Consensus theoretic classification methods, IEEE Transactions on Systems Man and Cybernetics 22, 688-704 (1992).
11. BERENSTEIN C., KANAL L. N. & LAVINE D., Consensus rules, in Uncertainty in Artificial Intelligence (Edited by L.N. KANAL and J.F. LEMMER). North Holland, New York (1986).
12. WINKLER R. L., Combining probability distributions from dependent information sources, Management Sciences, 27, 479-488 (1981).
13. BENEDIKTSSON J. A., SVEINSSON J. R., ERSOY O. K. & SWAIN P. H., Parallel consensual neural networks, to appear IEEE Transactions on Neural Networks.
14. RUCK D. W., ROGERS S. K., KABRISKY M., OXLEY M. E., & SUTER B. W., The multilayer perceptron as an approximation to a Bayes optimal discrimination function, IEEE Transactions on Neural Networks 1, 296-298 (1990).
15. COIFMAN R. R. & WICKERHAUSER M. V., Entropy-based algorithms for best basis selection, IEEE Transactions on Information Theory 38, 713-718 (1992).
16. BENEDIKTSSON J. A., SVEINSSON J. R. & SWAIN P. H., Hybrid consensus theoretic classification, Proceedings of the 1996 IEEE International Geoscience and Remote Sensing Symposium 3, 1848-1850 (1996).
17. MICHALEWICZ Z., Genetic Algorithms + Data Structures = Evolution Programs, 2nd extended edition, Springer-Verlag, New York (1994).
18. YAO L. & SEATHERS W. A., Nonlinear parameter estimation via the genetic algorithm, IEEE Transactions on Signal Processing 42, 927-935 (1994).
19. GOODENOUGH D. G., GOLDBERG M., PLUNKETT G. and ZELEK J., The CCRS SAR/MSS Anderson River Data Set, IEEE Transactions on Geoscience and Remote Sensing 25, 360-367, (1987).
20. SWAIN P. H., Fundamentals of pattern recognition in remote sensing in Remote Sensing - The Quantitative Approach (Edited by P. H. SWAIN and S. DAVIS), McGraw-Hill Book Company, New York (1978).
21. BARNARD E., Optimization for training neural nets, IEEE Transactions on Neural Networks 3, 232-240 (1992).
22. SILVERMAN B.W., Density Estimation for Statistics and Data Analysis, Monographs on Statistics and Applied Probability, Chapman and Hall, New York (1986).