Different ways of weakening decision trees and their impact on classification accuracy of DT combination

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

Volumn 1857 LNCS, Issue , 2000, Pages 200-209

  Latinne, Patrice ^a   Debeir, Olivier ^a   Decaestecker, Christine ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; DECISION TREES; STATISTICAL METHODS;

BOOTSTRAP REPLICATES; CLASSIFICATION ACCURACY; CLASSIFIER COMBINATION; CONVENTIONAL DATABASE; MAJORITY VOTING RULES; MULTIPLE FEATURES; PREDICTION ACCURACY; WEAK CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

EID: 84867057507     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45014-9_19     Document Type: Conference Paper

References (21)

1. Ali and Pazzani. Error reduction through learning multiple descriptions. Machine Learning, 24:173-202, 1996.
2. R. Battiti and A.M. Colla. Democracy in neural nets: voting schemes for classification. Neural Networks, 7(4):691-708, 1995.
3. Stephen D. Bay. Nearest neighbor classification from multiple feature subsets. In Proceedings of the International Conference on Machine Learning, Madison, Wisc., 1998. Morgan Kaufmann Publishers.
4. C. Blake, E. Keogh, and C.J. Merz. 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.
5. Leo Breiman. Bagging predictors. Machine Learning, 24, 1996.
6. Leo Breiman. Bias, variance and arcing classifiers. Technical report, Statistics department. University of California, 1996.
7. Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning, pages 1-22, 1999.
8. Ioav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In In Prod. of the 13th International Conference on Machine Learning, pages 146-148. Morgan Kaufmann, 1996.
9. T.K Ho, Jonathan J. Hull, and Sargur N. Srihari. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1):66-75, 1994.
10. Y. S. Huang and Ching Y. Suen. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 1995.
11. Joseph Kittler. Combining classifiers: a theoretical framework. Pattern Analysis and Applic., 1:18-27, 1998.
12. Ron Kohavi and Clayton Kunz. Option decision trees with majority votes. In Proceedings of the Fourtheeth International Conference on Machine Learning, pages 161-169, San Francisco, CA, 1997. Morgan Kaufmann.
13. Louisa Lam and Ching Y. Suen. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE tr. on systems, man and cybernetics, 27(5), 1997.
14. J.R. Quinlan. C4.5: Programs For Machine Learning. Morgan Kaufmann Publishers, San Mateo, California, 1993.
15. J.R. Quinlan. Bagging, boosting, and c4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 725-730, 1996.
16. G.H. Rosenfield and K. Fitzpatrick-Lins. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52(2):223-227, February 1986.
17. Bernard Rosner. Fundamentals of Biostatistics. Duxbury Press (ITP), Belmont, CA, USA, 4th edition, 1995.
18. S. Siegel and N.J. Castellan. Nonparametric Statistics for the behavioral sciences. McGraw-Hill, second edition, 1988.
19. K. Tumer and J. Ghosh. Classifier combining: analytical results and implications. In Proceedings of the National Conference on Artificial Intelligence, Portland, OR, 1996.
20. L. Xu, A. Krzyzak, and Ching Y. Suen. Methods of combining multiple classifiers and their applications to handwriting recogntion. IEEE tr. on systems, man and cybernetics, 22(3):418-435, 1992.
21. Zijian Zheng. Generating classifier committees by stochastically selecting both attributes and training examples. In Proceedings of the 5th Pacific Rim International Conferences on Artificial Intelligence (PRICAI'98), pages 12-23. Berlin: Springer-Verlag, 1998.