Meta-Learner for Unknown Attribute Values Processing: Dealing with Inconsistency of Meta-Databases

Journal of Intelligent Information Systems

Volumn 22, Issue 1, 2004, Pages 71-87

  Bruha, Ivan ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Base classifiers; Meta classifier; Meta combiner; Meta learning; Unknown attribute value processing

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; DECISION MAKING; LEARNING SYSTEMS; METADATA;

BASE CLASSIFIERS; UNKNOWN ATTRIBUTE VALUE PROCESSING;

DATA MINING;

EID: 0346502020     PISSN: 09259902     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1025880714026     Document Type: Conference Paper

References (18)

1. Berka, P. and Bruha, I. (1995). Various Discretizing Procedures of Numerical Attributes: Empirical Comparisons. In 8th European Conference Machine Learning, Workshop Statistics, Machine Learning, and Knowledge Discovery in Databases (pp. 136-141), Heraklion: Crete.
2. Boswell, R. (1990). Manual for CN2, Version 4.1. Turing Institute, Techn. Rept. P-2145/Rab/4/1.3.
3. Bruha, I. (2002). Unknown Attribute Values Processing by Meta-Learner. International Symposium on Methodologies for Intelligent Systems (ISMIS-2002), Lyon, France.
4. Bruha, I. and Franek, F. (1996). Comparison of Various Routines for Unknown Attribute Value Processing: Covering Paradigm. International Journal Pattern Recognition and Artificial Intelligence, 10(8), 939-955.
5. Bruha, I. and Kockova, S. (1994). A Support for Decision Making: Cost-Sensitive Learning System. Artificial Intelligence in Medicine, 6, 67-82.
6. Catlett, J. (1991). On Changing Continuous Attributes into Ordered Discrete Attributes. In Y. Kodratoff (Ed.), Machine Learning (EWSL-91) (pp. 164-178). Berlin Heidelberg: Springer-Verlag.
7. Cestnik, B. (1990). Estimating Probabilities: A Crucial Task in Machine Learning. ECAI-90.
8. Cestnik, B., Kononenko, I., and Bratko, I. (1987). ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users. In I. Bratko and N. Lavrac (Eds.), Progress in Machine Learning. Proc. EWSL'87. Sigma Press.
9. Clark, P. and Boswell, R. (1991). Rule Induction with CN2: Some Recent Improvements. EWSL'91 (pp. 151-163). Porto.
10. Clark, P. and Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 3, 261-283.
11. Fan, D.W., Chan, P.K., and Stolfo, S.J. (1996). A Comparative Evaluation of Combiner and Stacked Generalization. Workshop Integrating Multiple Learning Models, Portland: AAAI.
12. Kononenko, I. (1992). Combining Decisions of Multiple Rules. In B. du Boulay and V. Sgurev (Eds.), Artificial Intelligence V: Methodology, Systems, Applications (pp. 87-96). Elsevier Science Publ.
13. Kononenko, I. and Bratko, I. (1991). Information-Based Evaluation Criterion for Classifier's Performance. Machine Learning, 6, 67-80.
14. Lee, C. and Shin, D. (1994). A Context-Sensitive Discretization of Numeric Attributes for Classification Learning. In A. Cohn (Ed.), ECAI-94 (pp. 428-432). Amsterdam: John Wiley.
15. Murphy, P.M. and Aha, D.W. UCI Repository of Machine Learning Databases. Irvine, University of California, Dept. of Information and Computer Science.
16. Quinlan, J.R. (1986). Induction of Decision Trees. Machine Learning, 1, 81-106.
17. Quinlan, J.R. (1989). Unknown Attribute Values in ID3. International Conference ML, 164-8.
18. Quinlan, J.R. (1992). C4.5 Programs for Machine Learning. Morgan Kaufmann.