A dynamic classifier selection method to build ensembles using accuracy and diversity

Proceedings of the Ninth Brazilian Symposium on Neural Networks, SBRN'06

Volumn , Issue , 2006, Pages 36-41

  Santana, Alixandre ^a   Soares, Rodrigo G F ^a   Canuto, Anne M P ^a   De Souto, Marcilio C P ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SYSTEMS; NEURAL NETWORKS;

DYNAMIC CLASSIFIER SELECTION; ENSEMBLE MEMBERS;

CLASSIFICATION (OF INFORMATION);

EID: 34848892731     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SBRN.2006.1     Document Type: Conference Paper

References (24)

1. M. Askela. "Comparison of classifier selection methods for improving committee performance". In in Multiple Classifier Systems - 4th Int.Workshop, MCS 2003, vol. 2709, ser. Lecture Notes in Computer Science, T. Windeatt and F. Roli, Eds., Guilford, U.K., 2003, pp. 306-316.
2. R. Banfield, L. Hall, K. Bowyer, andW. Kegelmeyer, "A new ensemble diversity measure applied to thinning ensemble," in Multiple Classifier Systems - 4th Int.Workshop, MCS 2003, vol. 2709, ser. LNCS, T. Windeatt and F. Roli, Eds., Guilford, U.K., 2003, pp. 306-316.
3. Blake, C.L. and Merz, C.J.: UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science. (1998)
4. A Canuto.. Combining neural networks and fuzzy logic for applications in character recognition. PhD thesis, University of Kent, 2001
5. W W. Cohen. "Fast Effective Rule Induction", 'Machine Learning: Proceedings of the Twelfth International Conference'(ML), pp.115-123. 1995.
6. J Czyz and M Sadeghi and J Kittler and L Vandendorpe. Decision fusion for face authentication, Proc First Int Conf on Biometric Authentication, 686-693, 2004.
7. G. Giacinto anf F Roli. "An Approach to the Automatic Design of multiple classifier systems. Pattern Recognition Letters, 22: 25-33, 2001.
8. G. Giacinto and F. Roli, "Design of effective neural network ensembles for image classification", Image and Vision Computing Journal, 19(9-10), pp. 697-705, 2001.
9. Giacinto, G. and Roli, F. "Dynamic Classifier Selection based on Multiple Classifier Behaviour". Patt Rec, 34, 1879-1881, 2001
10. Kuncheva, L. Switching Between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Trans on Systems, Man and Cybernetics - B 32(2), 146-155, 2002.
11. Kuncheva L.I. Combining Pattern Classifiers. Methods and Algorithms, Wiley, 2004.
12. Kuncheva L, Whitaker, C. Measures of diversity in classifier ensembles, Mach Learning, 51, 181-207, 2003.
13. A. Lazarevic, and Z Obradovic. Effective pruning of neural network classifier ensembles. Proc. Of the International Joint conference on Neural Networks, vol. 2, pp.796-801, 2001.
14. Lemieux, A and Parizeau, M "Flexible multi-classifier architecture for face recognition systems". The 16th International Conference on Vision Interface, 2003
15. D. Marginenatu and T. Dietterich, "Prununig adaptive boosting," in Proc. 14th Int. Conf. Machine Learning, San Francisco, CA, 1997, pp.378-387.
16. T Mitchell. Machine Learning. McGraw-hill, 1997
17. A J C Sharkey. Multi-net System. In Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems, (Ed) A. J. C. Sharkey. Spring-Verlag, 1-30, 1999.
18. Soares, R.; Santana, A. Canuto, A. and Soouto, M. Using Accuracy and Diversity to Select Classifiers to Build Ensembles. Accepted to IJCNN 2006.
19. A Tsymbal, M Pechenizkiy and P Cunningham. Diversity in search strategies for ensemble feature selection. Inf Fusion, Special issue on Diversity in Multiple Classifier System. 6 (1), pp. 83-98, 2005.
20. G. Valentino. An Experimental Bias-Variance Analysis of SVM Ensembles Based on Resampling Techniques. IEEE Transactions on System, Man and Cybernetics- Part B: Cybernetics, Vol. 35, No. 6, pp.1252-1271, 2005
21. T. Windeatt, Diversity measures for multiple classifier system analysis and design. Inf Fusion, Special issue on Diversity in Multiple Classifier System, pp. 21-36, 6 (1), 2005.
22. Woods, K. Kegelmeyer, W and Bowyer, K. Combination of Multiple Classifiers using Local Accuracy estimates, IEEE Trans on Patt Anal and Mach Intell, 19(4), 405-410, 1997.
23. X. Wu and Z. Chen. "Recognition of Exon/Intron Boundaries Using Dynamic Ensembles". Proc. of the Computational Systems Bioinformatics Conference (CSB), 2004
24. Zhou,D, J. and Zhang, "Face recognition by combining several algorithms" Pattern Recognition 3(3):497-500, 2002.