On data and algorithms: Understanding inductive performance

Machine Learning

Volumn 54, Issue 3, 2004, Pages 275-312

  Kalousis, Alexandros ^a   Gama, João ^b   Hilario, Melanie ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

^b UNIVERSITY OF PORTO   (Portugal)

Author keywords

Classification; Classifier ranking; Clustering classifiers; Clustering datasets; Error correlation; Meta learning

Indexed keywords

CORRELATION METHODS; LEARNING ALGORITHMS; MEASUREMENT ERRORS; NUMERICAL METHODS; PERFORMANCE;

CLASSIFICATION ALGORITHMS; DATASETS; ERROR CORRELATION; META LEARNING;

LEARNING SYSTEMS;

EID: 1642280141     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:MACH.0000015882.38031.85     Document Type: Article

References (31)

1. Agresti, A. (1990). Categorical Data Analysis. John Wiley and Sons: Series in Probability and Mathematical Statistics.
2. Aha, D. (1992). Generalizing from case studies: A case study. In D. Sleeman & P. Edwards (Eds.), Proceedings of the 9th International Machine Learning Conference (pp. 1-10). Morgan Kaufman.
3. Ali, K., & Pazzani, M. (1996). Error reduction through learning multiple descriptions. Machine Learning, 24, 1996.
4. Bay, S., & Pazzani, M. (2000). Characterizing model errors and differences. In P. Langley (Ed.), Proceedings of the 17th International Conference on Machine Learning (pp. 49-56). Morgan Kaufman.
5. Bensusan, H. (1999). Automatic Bias Learning: An Inquiry Into the Inductive Basis of Induction. Doctoral dissertation, University Of Sussex, Cognitive Science.
6. Bock, H., & Diday, E. (2000). Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer-Verlag.
7. Brazdil, P., Carlos, S., & Costa, J. (2003). Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning, 50, 251-277.
8. Brodley, C. (1994). Recursive Automatic Algorithm Selection for Inductive Learning. Doctoral dissertation, University of Massachusetts.
9. Cohen, W. (1995). Fast effective rule induction. In A. Priedities & S. Russell (Eds.), Proceedings of the 12th International Conference on Machine Learning (pp. 115-123). Morgan Kaufman.
10. Cristianini, N., & Shawe-Taylor, J. (2002). An Introduction to Support Vector Machines. Cambridge University Press.
11. Duda, R., Hart, P., & Stork, D. (2001). Pattern Classification and Scene Analysis. John Willey and Sons.
12. Gama, J., & Brazdil, P. (1995). Characterization of classification algorithms. In C. Pinto-Ferreira & N. Mamede (Eds.), Proceedings of the 7th Portugese Conference in AI, EPIA 95 (pp. 83-102). Springer-Verlag.
13. Gama, J., & Brazdil, P. (1999). Linear tree. Intelligent Data Analysis, 3, 1-22.
14. Hirschberg, D. S., Pazzani, M. J., & Ali, K. M. (1994). Average Case Analysis of k-CNF and k-DNF Learning Algorithms, vol. II: Intersections Between Theory and Experiment, 15-28. MIT Press.
15. Kalousis, A., & Hilario, M. (2001a). Feature selection for meta-learning. In D. Cheung, G. Williams & Q. Li (Eds.), Proceedings of the 5th Pasific Asia Conference on Knowledge Discovery and Data Mining (pp. 222-233). Springer.
16. Kalousis, A., & Hilario, M. (2001b). Model selection via meta-learning: A comparative study. International Journal On Artificial Intelligence Tools, 10.
17. Kalousis, A., & Theoharis, T. (1999). Noemon: Design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis, 3, 319-337.
18. Langley, P., & Sage, S. (1999). Tractable average-case analysis of naive Bayesian classifiers. In I. Bratko & S. Dzeroski (Eds.), Proceedings of the 16th International Conference on Machine Learning (pp. 220-228). Morgan Kaufmann.
19. Langley, P., & Iba, W. (1993). Average-case analysis of a nearest neighbor algorithm. In R. Bajcsy (Ed.), Proceedings of the 13th International Joint Conference on AI (pp. 889-894). Morgan Kaufmann.
20. METAL project (2002). http://www.metal-kdd.org.
21. Michie, D., Spiegelhalter, D., & Taylor, C. (1994). Machine Learning, Neural and Statistical Classification, Ellis Horwood Series in Artificial Intelligence.
22. Peng, Y., Flach, P., Soares, C., & Brazdil, P. (2002). Improved dataset characterisation for meta-learning. In S. Lange & K. Satoh (Eds.), Proceedings of the 5th International Conference on Discovery Science 2002 (pp. 141-152). Springer-Verlag.
23. Pfahringer, B., Bensusan, H., & Giraud-Carrier, C. (2000). Tell me who can learn you and I can tell you who you are: Landmarking various learning algorithms. In P. Langley (Ed.), Proceedings of the 17th International Conference on Machine Learning (pp. 743-750). Morgan Kaufman.
24. Quinlan, J. (1992). C4.5: Programs for Machine Learning. Morgan Kaufman Publishers.
25. Quinlan, R. (2000). http://www.rulequest.com/see5-info.html.
26. Scheffer, T. (2000). Average-case analysis of classification algorithms for boolean functions and decision trees. In H. Arimura, S. Jain & A. Sharma (Eds.), Proceedings of the 11th International Conference Algorithmic Learning Theory (pp. 194-208), Springer Verlag, LNCS 1968.
27. Soares, C., & Brazdil, P. (2000). Zoomed ranking: Selection of classification algorithms based on relevant performance information. In D. Zighed, J. Komorowski & J. Zytkow (Eds.), Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 126-135). Springer.
28. Todorovski, L., Blockeel, H., & Dzeroski, S. (2002). Ranking with predictive clustering trees. In T. Elomaa, H. Mannila and H. Toivonen (Eds.), Proceedings of the 13th European Conference on Machine Learning (pp. 444-455). Springer.
29. Todorovski, L., & Dzeroski, S. (1999). Experiments in meta-level learning with ILP. In J. Zytkow & J. Rauch (Eds.), Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery (pp. 98-106). Springer.
30. Tumer, K., & Ghosh, J. (1995). Classifier combining: Analytical results and implications. In In AAAI-95-Workshop in Induction of Multiple Learning Models.
31. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241-259.