Comparing classifiers and metaclassifiers

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

Volumn 6870 LNAI, Issue , 2011, Pages 56-65

  Lozano, Elio ^a   Acuña, Edgar ^b  

^a UNIVERSITY OF PUERTO RICO   (Puerto Rico)

^b UNIVERSITY OF PUERTO RICO   (Puerto Rico)

Author keywords

Classifiers; Ensembles

Indexed keywords

BASE CLASSIFIERS; CLASSIFICATION ERROR RATE; DATA SETS; ENSEMBLES; MACHINE-LEARNING DATABASE; META-CLASSIFIERS; MULTIPLE BASE CLASSIFIERS; COMPARING CLASSIFIERS; GENERATIVE METHODS; STRONG CLASSIFIERS;

DATA MINING;

CLASSIFIERS; LEARNING SYSTEMS;

EID: 80052220155     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23184-1_5     Document Type: Conference Paper

References (13)

1. Amin, F., Islam, M., Murase, K.: Ensemble of single-layered complex-valued neural networks for classification tasks. Neurocomputing 72, 2227-2234 (2009)
2. Blake, C.L., Mertz, C.J., Newman, D.J., Hettich, C.L.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/ MLRepository.html
3. Breiman, L.: Bagging predictors. Machine Learning, Technical Report. Department of Statistics, University of California (1994)
4. Dietterich, T.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
5. Duin, W., Tax, D.: Experiments with Classifier Combining Rules. In: Pattern Recognition Group, pp. 16-29. University of Technology and Springer, The Netherlands, Heidelberg (2000)
6. Freund, Y., Schapire, R.: Experiments with a new booosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148-156. Morgan Kauffman, San Francisco (1996)
7. Goumas, S.K., Dimou, I.N., Zervakis, M.E.: Combination of multiple classifiers for post-placement quality inspection of components: A comparative study. Information Fusion 11(2), 149-162 (2010)
8. Hsieh, N.C., Hung, L.P.: A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications 37(1), 534-545 (2010)
9. Oza, N.C., Tumer, K.: Classifier ensembles: Select real-world applications. Information Fusion 9(1), 4-20 (2008)
10. Quinlan, R.: C4.5 Programs for Machine Learning. Published by Morgan Kaufmann Series in Machine Learning (1993)
11. Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifier Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78-87. Springer, Heidelberg (2001) (Pubitemid 33303215)
12. Tumer, K., Ghosh, J.: Robust Order Statistics-based ensembles for distributed data mining. In: Advances in Distributd and Parallel Knowledge Discovery. AAAI Press/The MIT Press (2000)
13. Valentini, G., Masulli, F.: Ensembles of Learning Machines. In: Marinaro, M., Tagliaferri, R. (eds.) WIRN 2002. LNCS, vol. 2486, pp. 3-19. Springer, Heidelberg (2002)