Exploiting diversity in ensembles: improving the performance on unbalanced datasets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4472 LNCS, Issue , 2007, Pages 397-406

  Chawla, Nitesh V ^a   Sylvester, Jared ^b  

^a UNIVERSITY OF NOTRE DAME   (United States)

^b University of Maryland ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; FUNCTION EVALUATION; GENETIC ALGORITHMS; LEARNING SYSTEMS;

DATA SET; FITNESS FUNCTIONS; VOTING SCHEMES;

DATA STRUCTURES;

EID: 37349031558     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72523-7_40     Document Type: Conference Paper

References (18)

1. Y. Freund and R. Schapire, "Experiments with a new boosting algorithm," in Thirteenth International Conference on Machine Learning, 1996.
2. L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
3. T. Dietterich, "Ensemble methods in machine learning," Lecture Notes in Computer Science, vol. 1857, pp. 1-15, 2000.
4. L. Kuncheva and C. Whitaker, "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy," Machine Learning, vol. 51, pp. 181-207, 2003.
5. J. Sylvester and N. V. Chawla, "Evolutionary ensemble creation and thinning," in International Joint Conference on Neural Networks, pp. 5148-5155, 2006.
6. C. J. van Rijsbergen, Information Retrieval. London: Butterworths, 1979.
7. N. Chawla, L. Hall, B. K.W., and W. Kegelmeyer, "SMOTE: Synthetic Minority Oversampling TEchnique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
8. F. Provost and T. Fawcett, "Robust Classification for Imprecise Environments," Machine Learning, vol. 42/3, pp. 203-231, 2001.
9. F. Provost and P. Domingos, "Tree induction for probability-based rankings," Machine Learning, vol. 52(3), 2003.
10. D. Opitz, "Feature selection for ensembles," in AAAI/IAAI, pp. 379-384, 1999.
11. C. Guerra-Salcedo and L. Whitley, "Genetic approach to feature selection for ensemble creation," in International Conference on Genetic and Evolutionary Computation, pp. 236-243, 1999.
12. J. Yang and V. Honavar, "Feature subset selection using A genetic algorithm," in Genetic Programming 1997: Proceedings of the Second Annual Conference, p. 380, 13-16 1997.
13. Y. Kim, N. Street, and F. Menczer, "Meta-evolutionary ensembles," in IEEE Intl. Joint Conf. on Neural Networks, pp. 2791-2796, 2002.
14. F. Menczer, W. N. Street, and M. Degeratu, "Evolving heterogeneous neural agents by local selection," in Advances in the Evolutionary Synthesis of Neural Systems (V. Honavar, M. Patel, and K. Balakrishnan, eds.), Cambridge, MA: MIT Press, 2000.
15. Y. Liu, X. Yao, and T. Higuchi, "Evolutionary ensembles with negative correlation learning," IEE Transactions on Evolutionary Computation, vol. 4.4, pp. 380-387, 2000.
16. L. I. Kuncheva and L. C. Jain, "Designing classifier fusion systems by genetic algorithms," IEEE-EC, vol. 4, pp. 327-336, November 2000.
17. W. B. Langdon and B. F. Buxton, "Genetic programming for combining classifiers," in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 66-73, 2001.
18. D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE Transactions on Evolutionary Computation, vol. 1, pp. 67-82, April 1997.