Combining bagging, boosting and dagging for classification problems

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

Volumn 4693 LNAI, Issue PART 2, 2007, Pages 493-500

  Kotsianti, S B ^a   Kanellopoulos, D ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Data mining; Ensembles of classifiers; Machine learning

Indexed keywords

BAGGING; ENSEMBLES OF CLASSIFIERS; NOISE FREE DATA; STANDARD BENCHMARK DATASETS;

DATA MINING; DATA STRUCTURES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBLEM SOLVING;

CLASSIFICATION (OF INFORMATION);

EID: 38049023534     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74827-4_62     Document Type: Conference Paper

References (16)

1. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36, 105-139 (1999)
2. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
3. Bosch, A., Daelemans, W.: Memory-based morphological analysis. In: proceedings of 37th Annual Meeting of the ACL, University of Maryland, pp. 285-292 (1999), http://ilk.kub.nl/~antalb/ltuia/week10.html
4. Breiman, L.: Bagging Predictors. Machine Learning 24(3), 123-140 (1996)
5. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
6. Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: proceedings of ICML'96, pp. 148-156 (1996)
7. Iba, W., Langley, P.: Induction of one-level decision trees. In: proceedings of Ninth International Machine Learning Conference (1992). Aberdeen, Scotland (1992)
8. Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Preventing student dropout in distance learning systems using machine learning techniques. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774, pp. 267-274. Springer, Heidelberg (2003)
9. Melville, P., Mooney, R.: Constructing Diverse Classifier Ensembles using Artificial Training Examples. In: proceedings of IJCAI-2003, Acapulco, Mexico, pp. 505-510 (August 2003)
10. Murthy: Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2, 345-389 (1998)
11. Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Artificial Intelligence Research 11, 169-198 (1999)
12. Salzberg, S.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1, 317-328 (1997)
13. Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26, 1651-1686 (1998)
14. Ting, K.M., Witten, I.H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, pp. 367-375 (1997)
15. Webb, G.I.: MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40, 159-196 (2000)
16. Witten Ian, H., Eibe, F.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)