Learning decision rules from data streams

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1255-1260

  Gama, João ^a   Kosina, Petr ^a,b  

^a UNIVERSITY OF PORTO   (Portugal)

^b MASARYK UNIVERSITY   (Czech Republic)

Author keywords

Data streams; Rule learning

Indexed keywords

DATA MINING TASKS; DATA STREAM; DECISION RULES; EXPERIMENTAL EVALUATION; INTERPRETABILITY; RULE LEARNING; RULE SET; STREAM MINING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA COMMUNICATION SYSTEMS;

DATA MINING;

EID: 84863595229     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-213     Document Type: Conference Paper

References (18)

1. Adä, I. and M. R. Berthold (2010). The new iris data: modular data generators. In KDD, pp. 413-422.
2. Clark, P. and T. Niblett (1989). The CN2 induction algorithm. Machine Learning 3, 261-283.
3. Cohen, W. (1995). Fast effective rule induction. In Proceedings of the ICML, 115-123. Morgan Kaufmann.
4. Domingos, P. (1996). Unifying instance-based and rule-based induction. Machine Learning 24, 141-168.
5. Ferrer, F., J. Aguilar, and J. Riquelme (2005). Incremental rule learning and border examples selection from numerical data streams. Journal of Universal Computer Science 11(8), 1426-1439.
6. Frank, E. and I. H. Witten (1998). Generating accurate rule sets without global optimization. In Proceedings of ICML, pp. 144-151. Morgan Kaufmann.
7. Gama, J., R. Fernandes, and R. Rocha (2006). Decision trees for mining data streams. Intelligent Data Analysis 10(1), 23-46.
8. Gama, J., R. Rocha, and P. Medas (2003). Accurate decision trees for mining high-speed data streams. In Proceedings of KDD, pp. 523-528. ACM Press.
9. Gama, J., R. Sebastião, and P. P. Rodrigues (2009). Issues in evaluation of stream learning algorithms. In Proceedings of KDD, pp. 329-338.
10. Kolter, J. Z. and M. A. Maloof (2003). Dynamic weighted majority: A new ensemble method for tracking concept drift. In Proceedings of ICDM, pp. 123-130. IEEE Computer Society.
11. Maloof, M. and R. Michalski (2004). Incremental learning with partial instance memory. Artificial Intelligence 154, 95-126.
12. Quinlan, R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc. San Mateo, CA.
13. Rivest, R. (1987). Learning Decision Lists. Machine Learning 2, 229-246.
14. Schlimmer, J. C. and R. H. Granger (1986). Incremental learning from noisy data. Machine Learning 1, 317-354.
15. Street, W. N. and Y. Kim (2001). A streaming ensemble algorithm SEA for large-scale classification. In Proceedings KDD, pp. 377-382. ACM Press.
16. Wang, H., W. Fan, P. S. Yu, and J. Han (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of KDD, pp. 226-235. ACM Press.
17. Weiss, S. and N. Indurkhya (1998). Predictive Data Mining, a practical Guide. Morgan Kaufmann Publishers.
18. Widmer, G. and M. Kubat (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101. (Pubitemid 126737384)