A new ensemble feature selection and its application to pattern classification

Journal of Control Theory and Applications

Volumn 7, Issue 4, 2009, Pages 419-426

  Zhang, Dongbo ^a   Wang, Yaonan ^b  

^a XIANGTAN UNIVERSITY   (China)

^b HUNAN UNIVERSITY   (China)

Author keywords

Ensemble feature selection; Neural network ensemble; Remote sensing image classification; Rough sets reduction

Indexed keywords

BASE CLASSIFIERS; CLASSIFICATION ACCURACY; COMPONENT NETWORKS; COMPUTING COMPLEXITY; DATA SETS; DYNAMIC REDUCTION; ENSEMBLE FEATURE SELECTION; ENSEMBLE FEATURE SELECTIONS; GENERALIZATION ABILITY; MULTIPLE BP NEURAL NETWORKS; NEURAL NETWORK ENSEMBLE; NEURAL NETWORK ENSEMBLES; PATTERN CLASSIFICATION; REDUCTS; REMOTE SENSING IMAGE CLASSIFICATION; REMOTE SENSING IMAGES; RESAMPLING METHOD; ROUGH SET; ROUGH SETS REDUCTION; SEARCH STRATEGIES; SELECTIVE ENSEMBLES;

COMPUTATIONAL COMPLEXITY; FUZZY SETS; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; NEURAL NETWORKS; REMOTE SENSING; ROUGH SET THEORY;

IMAGE CLASSIFICATION;

EID: 70350461904     PISSN: 16726340     EISSN: 10008152     Source Type: Journal    
DOI: 10.1007/s11768-009-7234-z     Document Type: Article

References (26)

1. L. K. Hansen, P. Salamon. Neural network ensembles[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10): 993-1001.
2. Z. Zhou, S. Chen. Neural network ensemble[J]. Chinese Journal of Computers, 2002, 25(1): 1-8.
3. A. Krogh, J. Vedelsby. Neural network ensembles, cross validation, and active learning[M]//Advances in Neural Information Processing Systems 7. Cambridge: MIT Press, 1995: 231-238.
4. J. Hampshire, A. Waibel. A novel objective function for improved phoneme recognition using time delay neural networks[J]. IEEE Transactions on Neural Networks, 1990, 1(2): 216-228.
5. K. J. Cherkauer. Human expert level performance on a scientific image analysis task by a system using combined artificial neural networks[C]//Proceedings of the 13th AAAI Work Shop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms. Portland: AAAI Press, 1996: 15-21.
6. R. Maclin, J. W. Shavlik. Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks[C]//The 14th International Joint Conference on Artificial Intelligence. San Mateo: Morgan Kaufmann Publishers, 1995: 524-530.
7. X. Yao, Y. Liu. Making use of population information in evolutionary artificial neural networks[J]. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 1998, 28(3): 417-425.
8. Z. Zhou, J. Wu, Y. Jiang, et al. Genetic algorithm based selective neural network ensemble[C]//The 17th International Joint Conference on Artificial Intelligence. Seattle, Washington, 2001: 797-802.
9. S. Zeke, H. Chan, N. Kasabov. Fast neural network ensemble learning via negative-correlation data correction[J]. IEEE Transactions on Neural Networks, 2005, 16(6): 1707-1710.
10. X. Fu, B. Feng, Z. Ma, et al. Method of incremental construction of heterogenous neural network ensemble with negeative correlation[J]. Journal of Xi'an Jiangtong University, 2004, 38(8): 796-799.
11. K. Li, H. Huang. A selective approach to neural network ensemble based on clustering technology[J]. Journal of Computer Research and Devlopment, 2005, 42(4): 594-598.
12. Q. Fu, S. Hu, S. Zhao. Clustering-based selective neural network ensemble[J]. Journal of Zhejiang University, 2005, 6A(5): 387-392.
13. Y. Freund, R. E. Schapire. A decision-theoretic generalization of online learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
14. L. Breiman. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
15. T. K. Ho. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.
16. D. Opitz. Feature selection for ensembles[C]//The 16th National Conference on Artificial Intelligence. Menlo Park: AAAI Press, 1999: 379-384.
17. J. Ling, Z. Chen, Z. Zhou. Feature selection based neural network ensemble method[J]. Journal of Fudan University (Natural Science), 2004, 43(5): 685-688.
18. P. Cunningham, J. Carney. Diversity versus quality in classification ensembles based on feature selection[C]//The 11th European Conference on Machine Learning. Berlin: Springer-Verlag, 2000: 109-116.
19. A. Tsymbal, M. Pechenizkiy, P. Cunningham. Diversity in Ensemble Feature Selection[R]. Dublin: Trinity College Dublin, 2003.
20. S. Vinterbo, A. Ohrn. Minimal approximate hitting sets and rule templates[J]. International Journal of Approximate Reasoning, 2000, 25(2): 123-143.
21. J. Bazan, A. Skowron, P. Synak. Dynamic reducts as a tool for extracting laws from decision tables[C]//Proceedings of Symposium on Methodologies for Intelligent Systems. Berlin: Springer-Verlag, 1994: 346-355.
22. Y. Liu, X. Yao. Simultaneous training of negatively correlated neural networks in an ensemble[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1999, 29(6): 716-725.
23. Z. Zhou, J. Wu, W. Tang. Ensembling neural networks: many could be better than all[J]. Artificial Intelligence, 2002, 137(1/2): 239-263.
24. J. Wu, Z. Zhou, X. Shen, et al. A selective constructing approach to neural network ensemble[J]. Journal of Computer Research & Devlopment, 2000, 37(9): 1039-1044.
25. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/.
26. D. W. Aha, R.L. Bankert. A comparative evaluation of sequential feature selection algorithms[C]//The 5th International Workshop on Artificial Intelligence and Statistics. Berlin: Springer-Verlag, 1995: 199-206.