Drifting concepts as hidden factors in clinical studies

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

Volumn 2780 LNAI, Issue , 2003, Pages 355-364

  Kukar, Matjaž ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Clinical studies; Concept drift; Data mining; Gradual forgetting; Machine learning; Partial memory learning; Windowing

Indexed keywords

CLINICAL STUDY; CONCEPT DRIFTS; GRADUAL FORGETTING; MEMORY LEARNING; WINDOWING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; MEDICAL COMPUTING; DATA MINING; DATABASE SYSTEMS; DISEASES; STATISTICAL METHODS;

DATA MINING; CLINICAL LABORATORIES;

DATA MINING ALGORITHMS; DATA MINING TOOLS; DISEASE DIAGNOSTICS;

EID: 7444250214     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39907-0_49     Document Type: Conference Paper

References (15)

1. th Intl. Conf. on Machine Learning ICML'95, pages 115-123, San Francisco, California, USA, 1995. Morgan Kaufmann.
2. F. Esposito, D. Malerba, and G. Semeraro. Simplifying decision trees by pruning and grafting: new results. In N. Lavrac and S. Wrobel, editors, Proc. Europ. Conf. on Machine Learning ECML'95, pages 287-290. Springer Verlag, 1995.
3. I. Grabtree and S. Soltysiak. Identifying and tracking changing interests. International Journal of Digital Libraries, 2:38-53, 1998.
4. C. Grošelj, M. Kukar, J. Fettich, and I. Kononenko. Machine learning improves the accuracy of coronary artery disease diagnostic methods. In Proc. Computers in Cardiology, volume 24, pages 57-60, Lund, Sweden, 1997.
5. M. B. Harries, C. Sammut, and K. Horn. Extracting hidden context. Machine Learning, 32:101-126, 1998.
6. D. P. Helmbold and P. M. Long. Tracking drifting concepts by minimizing disagreements. Machine Learning, 14:27-45, 1994.
7. th ACM SIGKDD Inter. Conf. on Knowledge Discovery and Data Mining, pages 97-106, San Francisco, CA, 2001. ACM Press.
8. R. Klinkenberg and T. Joachims. Detecting concept drift with support vector machines. In P. Langley, editor, Proceedings of ICML-00, 17th International Conference on Machine Learning, pages 487-494, Stanford, US, 2000. Morgan Kaufmann Publishers, San Francisco, US.
9. I. Koychev. Gradual forgetting for adaptation to concept drift. In Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning, pages 101-106, Berlin, Germany, 2000.
10. M. Kukar. Making reliable diagnoses with machine learning: A case study. In S. Quaglini, P. Barahona, and S. Andreassen, editors, Proceedings of Artificial Intelligence in Medicine Europe, AIME 2001, pages 88-96, Cascais, Portugal, 2001. Springer.
11. M. Kukar and I. Kononenko. Reliable classifications with Machine Learning. In Proceedings of 13th European Conference on Machine Learning, ECML 2002, pages 219-231, 2002.
12. M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, and J. Fettich. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine, 16 (1):25-50, 1999.
13. M. A. Maloof and R. S. Michalski. Selecting examples for partial memory learning. Machine Learning, 41(1):27-52, 2000.
14. N. A. Syed, H. Liu, and K. K. Sung. Handling concept drifts in incremental learning with support vector machines. In Knowledge Discovery and Data Mining, pages 317-321, 1999.
15. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1):69-101, 1996.