Dynamic classifier selection for effective mining from noisy data streams

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 305-312

  Zhu, Xingquan ^a   Wu, Xindong ^a   Yang, Ying ^a  

^a University of Vermont   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; EVALUATION; LEARNING SYSTEMS; RELIABILITY; SET THEORY; SPURIOUS SIGNAL NOISE;

DISJOINT SUBSETS; DYNAMIC CLASSIFIER SELECTION (DCS) MECHANISM; NOISY DATA STREAMS; SENSOR NETWORKING;

DATA MINING;

EID: 19544364128     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. AH K. & Pazzani M., Error reduction through learning multiple description, Machine Learning, vo.24, no.3, 1996.
2. Huang Y. S. & Suen C. Y., A method of combining multiple experts for the recognition of unconstrained handwritten numerals, IEEE Trans. on PAMI 17(1), pp. 90-94, 1995.
3. Ueda N., Optimal linear combination of neural networks for improving classification perform., IEEE Trans. on PAMI, 22, 2000.
4. Xu L., Krzyak A. & Suen C., Methods of combining multiple classifiers and their application to handwriting recognition, IEEE Trans. on Sys., Man and Cyber., 22, 1992.
5. Schaffer C., Selecting a classification method by cross-validation, Machine Learning, vol. 13, pp. 135-143, 1993.
6. Breiman L., Friedman J. H., Olshen R.A. & Stone C. J., Classification and regression trees, Belmont, CA, 1984.
7. Merz C. J., Dynamical selection of learning algorithms, In: D.Fisher, H.-J.Lenz (eds.). Learning from Data, Artificial Intelligence and Statistics, Springer-Verlag, NY(1996).
8. Merz C. J., Using correspondence analysis to combine classifiers, Machine Learning, vo.36 (1-2), pp.33-58, 1999.
9. Ortega J., Koppel M. & Argamon S., Arbitrating among competing classifiers using learned referees, Knowledge and Information Systems, vol.3, no.4, 2001.
10. Woods K., Kegelmeyer W. P. & Bowyer K., Combination of multiple classifiers using local accuracy estimation, IEEE Transactions on PAMI, vol.19, no.4, Apr., 1997.
11. Wang H., Fan W., Yu P. & Han J., Mining concept-drifting data streams using ensemble classifiers, Proc. of KDD 2003.
12. Breiman L., Stacked regressions, Machine Learning, 24, 1996.
13. Schapire R., The strength of weak learnability, Machine Learning, vol.5, no.2, pp.197-227, 1990.
14. Chan P., An extensible meta-leaming approach for scalable and accurate inductive learning, Ph.D thesis, Columbia Univ., 1996.
15. Jain Anil K. & Dubes R. C., Algorithms for clustering data. Prentice Hall, 1998.
16. Kucheva L.I., Switching between selection and fusion in combining classifiers: An experiment, IEEE Trans. SMC, 32(2), 2002.
17. Holte R.C., Very simple classification rules perform well on most commonly used datasets, Machine Learning, 11, 1993.
18. th ICML Conf., Washington DC, 2003.
19. IBM Almaden Research, Synthetic data generator, http://www.almaden.ibm.com/software/quest/Resources/datasets/syndata. html#classSynData
20. Blake C. L. & Merz, UCI Data Repository, 1998.
21. Quinlan R., C4.5 programs for machine learning, San Mateo, CA, Morgan Kaufmann publisher, 1993.
22. Domingos P. & Hulten G., Mining high-speed data streams, Proc. of SIGKDD, 2000.
23. Nasraoui O., Cardona C., Rojas C. & Gonzalez F., TECNO-STREAMS: Tracking Evolving Clusters in Noisy Data Streams with a Scalable Immune System Learning Model, Proc. of ICDM, 2003.
24. Kolter J. & Maloof M., Dynamic weighted majority: a new ensemble method for tracking concept drift, Proc. of ICDM, 2003
25. Zhu X. & Wu X., Class noise vs attribute noise: A quantitative study of their impacts, Artificial Intelligence Review, in press, 2004.