Ensembles of biased classifiers

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 425-432

  Khoussainov, Rinat ^a   Heß, Andreas ^a   Kushmerick, Nicholas ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; PERFORMANCE; PERSONNEL TRAINING;

ITERATIVE STRUCTURE; ROC SPACES; TRISKEL WORKS;

LEARNING SYSTEMS;

EID: 31844454923     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102405     Document Type: Conference Paper

References (14)

1. Akbani, R., Kwek, S., & Japkowicz, N. (2004). Applying Support Vector Machines to Imabalanced Datasets. 15th European Conference on Machine Learning. Pisa.
2. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
3. Breiman, L. (1999). Pasting Small Votes for Classification in Large Databases and On-Line. Machine Learning, 36, 85-103.
4. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 341-378.
5. Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. First International Workshop on Multiple Classifier Systems. New York.
6. Ferri, C., Flach, P., & Hernandez-Orallo, J. (2004). Delegating Classifiers. 21st International Conference on Machine Learning. Banf.
7. Flach, P., & Wu, S. (2005). Repairing concavities in ROC curves. 19th International Joint Conference on AI. Edinburgh.
8. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121, 256-285.
9. Freund, Y., & Shapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning. Journal of Computer and System Sciences, 55, 119-139.
10. Fürnkranz, J. (1999). Separate and Conquer Rule Learning. Artificial Intelligence Review, 13, 3-54.
11. Platt, J. C. (1999). Fast Training of Support Vector Machines using Sequential Minimal Optimization, chapter 12, 185-208. MIT Press.
12. Shapire, R. E. (1990). The Strength of Weak Learnability. Machine Learning, 5, 197-227.
13. Vapnik, V. N. (1982). Estimation of dependencies based on empirical data. New York / Berlin: Springer Verlag.
14. Witten, I. H., & Frank, E. (1999). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.