Effects of domain characteristics on instance-based learning algorithms

Theoretical Computer Science

Volumn 298, Issue 1, 2003, Pages 207-233

  Okamoto, Seishi ^a   Yugami, Nobuhiro ^a  

^a FUJITSU LABORATORIES LTD   (Japan)

Author keywords

Average case analysis; Expected accuracy; Instance based learning; k Nearest neighbor classifier; Optimal value of k

Indexed keywords

ARTIFICIAL INTELLIGENCE; PATTERN RECOGNITION; PROBABILITY;

INSTANCE-BASED LEARNING;

LEARNING ALGORITHMS;

EID: 0037418779     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(02)00424-3     Document Type: Article

References (33)

1. D. Aha, D. Kibler, Noise-tolerant instance-based learning algorithms, in: Proc. 11th Internat. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1989, pp. 794-799.
2. Aha D., Kibler D., Albert M. Instance-based learning algorithms. Mach. Learning. 6:1991;37-66.
3. M. Albert, D. Aha, Analyses of instance-based learning algorithms, in: Proc. 9th National Conf. on Artificial Intelligence, AAAI Press, Menlo Park, CA, 1991, pp. 553-558.
4. Bailey T., Jain A. A note on distance-weighted. k -nearest neighbor rules IEEE Trans. Systems Man Cybernet. 8(4):1978;311-313.
5. Cover T. Estimation by the nearest neighbor rule. IEEE Trans. Inform. Theory. 14(1):1968;50-55.
6. Cover T., Hart P. Nearest neighbor pattern classification. IEEE Trans. Inform. Theory. 13(1):1967;21-27.
7. Creecy O., Masand B., Smith S., Waltz D. Trading MIPS and memory for knowledge engineering. Comm. Assoc. Comput. Mach. 35:1992;48-63.
8. B. Dasarathy (Ed.), Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Press, Los Altos, CA, 1991.
9. R. Duda, P. Hart (Eds.), Pattern Classification and Scene Analysis, Wiley, New York, 1973.
10. Dudani S. The distance-weighted. k -nearest-neighbor rule IEEE Trans. Systems Man Cybernet. 6(4):1976;325-327.
11. E. Fix, J. Hodges, Discriminatory analysis: nonparametric discrimination: consistency properties, Technical Report 4, USAF School of Aviation Medicine, Project 21-49-004, 1951.
12. Golea M., Marchand M. On learning perceptrons with binary weights. Neural Comput. 5:1993;767-782.
13. D. Haussler, Probably approximately correct learning, in: Proc. 8th National Conf. on Artificial Intelligence, AAAI Press. Menlo Park, CA, 1990, pp. 1101-1108.
14. D. Hirschberg, M. Pazzani, Average-case analysis of learning k -CNF concept, in: Proc. 9th Internat. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1992, pp. 206-211.
15. W. Iba, P. Langley, Induction of one-level decision trees, in: Proc. 9th Internat. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1992, pp. 233-240.
16. P. Langley, W. Iba, Average-case analysis of a nearest neighbor algorithm, in: Proc. 13th Internat. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1993, pp. 889-894.
17. P. Langley, W. Iba, K. Thompson, An analysis of bayesian classifiers, in: Proc. 10th National Conf. on Artificial Intelligence, AAAI Press, Menlo Park, CA, 1992, pp. 223-228.
18. P. Langley, S. Sage, Tractable average-case analysis of naive bayesian classifiers, in: Proc. 16th Internat. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1999, pp. 220-228.
19. P. Murphy, M. Pazzani, ID2- of -3: constructive induction of m -of- n concepts for discriminators in decision trees, in: Proc. 8th Internat. Workshop on Machine Learning, Morgan Kaufmann, Los Altos, CA, 1991, pp. 183-187.
20. S. Okamoto, K. Satoh, An average-case analysis of k -nearest neighbor classifier, in: Proc. 1st Internat. Conf. on Case-Based Reasoning, Lecture Notes in Artificial Intelligence, Vol. 1010, Springer, Berlin, 1995, pp. 253-264.
21. S. Okamoto, N. Yugami, Theoretical analysis of the nearest neighbor classifier in noisy domains, in: Proc. 13th Internat. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1996, pp. 355-363.
22. S. Okamoto, N. Yugami, An average-case analysis of the k -nearest neighbor classifier for noisy domains, in: Proc. 15th Internat. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1997, pp. 238-243.
23. S. Okamoto, N. Yugami, Generalized average-case analyses of the nearest neighbor algorithm, in: Proc. 17th Internat. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 2000, pp. 695-702.
24. Pazzani M., Sarrett W. A framework for average case analysis of conjunctive learning algorithms. Mach. Learning. 9:1992;349-372.
25. Pitt L., Valiant L. Computational limitations on learning from examples. J. Assoc. Comput. Mach. 35(4):1988;965-984.
26. J. Rachlin, S. Kasif, S. Salzberg, D. Aha, Toward a better understanding of memory-based reasoning systems, in: Proc. 11th Internat. Conf. on Machine Learning, Morgan Kaufmann, Los Altos, CA, 1994, pp. 242-250.
27. R. Reischuk, T. Zeugmann, A complete and tight average-case analysis of learning monomials, in: Proc. 16th Internat. Symp. on Theoretical Aspects of Computer Science, Lecture Notes in Computer Science, Vol. 1563, Springer, Berlin, 1999, pp. 414-423.
28. S. Salzberg, A. Delcher, D. Heath, S. Kasif, Learning with a helpful teacher, in: Proc. 12th Internat. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1991, pp. 705-711.
29. Salzberg S., Delcher A., Heath D., Kasif S. Best-case results for nearest neighbor learning. IEEE Trans. Pattern Anal. Mach. Intell. 17(6):1995;599-610.
30. Stanfill C., Waltz D. Toward memory-based reasoning. Comm. Assoc. Comput. Mach. 29(12):1986;1213-1228.
31. Valiant L. A theory of the learnable. Comm. Assoc. Comput. Mach. 27:1984;1134-1142.
32. D. Waltz, Massively parallel AI, in: Proc. 8th National Conf. on Artificial Intelligence, AAAI Press, Menlo Park, CA, 1990, pp. 1117-1122.
33. Wettschereck D., Aha D., Mohri T. A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11:1997;273-314.