Handling local concept drift with dynamic integration of classifiers: Domain of antibiotic resistance in nosocomial infections

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Volumn 2006, Issue , 2006, Pages 679-684

  Tsymbal, Alexey ^a   Pechenizkiy, Mykola ^b   Cunningham, Pádraig ^a   Puuronen, Seppo ^b  

^a TRINITY COLLEGE DUBLIN   (Ireland)

^b UNIVERSITY OF JYVÄSKYLÄ   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIBIOTICS; DATA HANDLING; DATABASE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS;

ANTIBIOTIC RESISTANCE; DYNAMIC INTEGRATION; NOSOCOMIAL INFECTIONS;

BIOMEDICAL ENGINEERING;

EID: 33845587386     PISSN: 10637125     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CBMS.2006.94     Document Type: Conference Paper

References (18)

1. Bauer E., Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants, Machine Learning, 36 (1,2), 1999, 105-139.
2. Brodley C., Lane T. Creating and exploiting coverage and diversity. In: Proc. AAAI-96 Workshop on Integrating Multiple Learned Models, Portland, OR, 1996, 8-14.
3. Delaney S.J., Cunningham P., Tsymbal A., Coyle L. A case-based technique for tracking concept drift in spam filtering. In: Proc. 24th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK, Springer, LNCS, 2004, 3-16.
4. Fan W. Systematic data selection to mine concept-drifting data streams. In: Proc. 10th Int. Conf. on Knowledge Discovery and Data Mining KDD'04, 2004.
5. Ferraro M.J., et al. Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically: approved standard; Performance standards for antimicrobial susceptibility testing. Wayne, PA: National Committee for Clinical Laboratory Standarts, NCCLS, 2005. (Documents M7-A6 and M100-S14, available at www.nccls.org).
6. Gaynes R.P. Surveillance of nosocomial infections: a fundamental ingredient for quality. Infect Control Hosp Epidemiol, 18(7), 1997, 475-478.
7. Klinkenberg R. Learning drifting concepts: example selection vs. example weighting, Intelligent Data Analysis, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, 8 (3), 2004.
8. Kolter J.Z., Maloof M.A. Dynamic weighted majority: a new ensemble method for tracking concept drift. In: 3rd IEEE Int. Conf. on Data Mining ICDM'03, IEEE CS Press, 2003, 123-130.
9. Schaffer C. Selecting a classification method by cross-validation, Machine Learning, 13, 1993, 135-143.
10. Stanley K.O. Learning concept drift with a committee of decision trees, Tech. Report UT-AI-TR-03-302, Dept of Computer Science, University of Texas at Austin, USA, 2003.
11. Street W., Kim Y. A streaming ensemble algorithm (SEA) for large-scale classification. In: Proc. 7th Int. Conf. on Knowledge Discovery and Data Mining KDD-2001, 2001, 377-382.
12. The Problem of Antibiotic Resistance, NIAID Fact Sheet. National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, U.S. Department of Health and Human Services, USA, 2004 (available at www.niaid.nih.gov/factsheets/antimicro.htm).
13. Tsymbal A. The problem of concept drift: definitions and related work, Tech. Report TCD-CS-2004-15, Dept. of Computer Science, Trinity College Dublin, Ireland, 2004.
14. Pechenizkiy M., Tsymbal A., Puuronen S., Shifrin M., Alexandrova I. Knowledge discovery from microbiology data: many-sided analysis of antibiotic resistance in nosocomial infections. In: 3rd Int. Conf. on Professional Knowledge Management: Experience and Visions (WM05), Springer, LNAI 3782, 2005, 360-372.
15. Tsymbal A., Puuronen S. Bagging and boosting with dynamic integration of classifiers. In: D.A. Zighed, J. Komorowski, J. Żytkow (eds.), Principles of Data Mining and Knowledge Discovery, Proceedings of PKDD 2000, Springer, LNAI 1910, 2000, 116-125.
16. Wang H., Fan W., Yu P.S., Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proc. 9th Int. Conf. on Knowledge Discovery and Data Mining KDD'03, 2003, 226-235.
17. Widmer G., Kubat M. Learning in the presence of concept drift and hidden contexts, Machine Learning, 23 (1), 1996, 69-101.
18. Witten I., Frank E. Data Mining: Practical Machine Learning Tools With Java Implementations, San Francisco: Morgan Kaufmann, 2000.