Building algorithm profiles for prior model selection in knowledge discovery systems

International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications

Volumn 8, Issue 2, 2000, Pages 77-87

  Hilario, Melanie ^a   Kalousis, Alexandras ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; VECTORS;

KNOWLEDGE DISCOVERY SYSTEMS;

DATA MINING;

EID: 0033705380     PISSN: 09691170     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Aha, D W, Kibler, D and Albert, M K, "Instance-based learning algorithms".Machine Learning, 6(1):37-66, 1991.
2. Blum, A and Langley, P, "Selection of relevant features and examples in machine learning". Artificial Intelligence, 97:245-72, 1997.
3. Bradley, C and Utgoff P E, "Multivariate decision trees". Machine Learning, 19:45-77, 1995.
4. Cohen, W, "Fast Effective Rule Induction". Machine Learning: Proceedings of the Twelfth International Conference (ML-95). Morgan Kaufmann, 1995.
5. Dempster, A P, Laird, N M and Rubin, D B, "Maximum likelihood from incomplete data via the EM algorithm". Journal of the Royal Statistical Society, series B, Vol. 39, pp. 1-38, 1977.
6. Domingos, P and Pazzani, M, "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29(2/3): 103-30, 1997.
7. Fisher, D and McKusick, K B, " An empirical comparison of ID3 and backpropagation". Proc. International Joint Conf. Aritificial Intelligence, 1989, pages 788-93.
8. Gama, J and Brazdil, P, "Linear Tree". Intelligent Data Analysis, 3(1999), 1-22.
9. Geman, S, Bienenstock, E and Doursat, R, "Neural networks and the bias/variance dilemma". Neural Computation, 4:1-58, 1992.
10. John, G H and Langley, P, "Estimating continuous distributions in bayesian classifiers". In Proc. Eleventh Conference on Uncertainty in Artificial Intelligence, pp 338-45. Morgan Kaufmann, 1995.
11. Kohavi, R, Somerfield, D and Dougherty, J, "Data Mining Using MLC++'. International Conference on Tools with AI, pp 234-45. 1996.
12. Kononenko, I, "Inductive and Bayesian learning in medical diagnosis". Applied Artificial Intelligence, 7: 317-37, 1993.
13. Lakshminarayan, K, Harp, S, Goldman, R and Samad, T, "Imputation of missing data using machine learing techniques". KDD-96, pp 140-5, 1996.
14. Langley, P and Sage, S, "Scaling to domains with irrelevant features". In Computational Learning Theory and Natural Learning Systems. MIT Press, 1997.
15. Lim, T, Loh, W and Shih, Y, "A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms". To appear in Machine Learning.
16. Little, R J and Rubin, D B, Statistical Analysis with Missing Data. Wiley, 1987.
17. Michie, D, Spiegelhalter, D J and Taylor, C C, (editors). Machine learning, neural and statistical classification. Prentice-Hall, 1994.
18. Mooney, R., Shavlik, J, Towell, G and Gove, W, "An experimental comparison of symbolic and connectionist learning algorithms". IJCAI-89, pp 775-80.
19. Murphy, P M and Aha, D W, UCI Machine Learning Repository. Irvine, CA: University of California.
20. Nakhaeizadeh, G and Schnabl, A, "Development of multi-criteria metrics for evaluation of data mining algorithms". In Proc. Third International Conference on Knowledge Discovery and Data Mining, pp 37-42, Newport Beach, CA, 1997. AAAI Press.
21. Quinlan, J R, The effect of noise in concept learning, chapter 6, pp 149-66. Morgan Kaufmann, 1986.
22. Quinlan, J R, C4.5: Programs for Machine Learning. M. Kaufmann, 1993.
23. Ripley, B D, Pattern Recognition and Neural Networks. Cambridge University. Press, 1996.
24. Schaffer, C A, "Conservation law for generalization performance". In W. W. Cohen and H. Hirsh, editors, Proc. of the 11th International Conference on Machine Learning, pp 259-65, Rutgers, NJ, 1994. Morgan Kaufmann.
25. Setiono, R and Liu, H, "Understanding neural networks via rule extraction". In Proc. International Joint Conference on Artificial Intelligence, pp 480-5, Montreal, Canada, 1995. Morgan Kaufmann.
26. Shavlik, J W, Mooney, R J and Towell, G G, "Symbolic and neural learning algorithms: an experimental comparison". Machine Learning, 6: 111-43, 1991.
27. Towell, G G, "Symbolic knowledge and neural networks: insertion refinement and extraction". PhD thesis, Univ. of Wisconsin-Madison, Computer Science Dept., 1992.
28. Utgoff, P E, "Incremental induction of decision trees". Machine Learning, 4: 161-186, 1989.
29. Weiss, S M and Kapouleas, I, "An empirical comparison of pattern recognition, neural nets, and machine learning classification methods". IJCAI-89, pp 781-6.
30. Wettschereck, D, Aha, D W and Mohri, T, "A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms". Artificial Intelligence Review, 11: 273-314, 1997.