Setting attribute weights for nearest neighbor learning algorithms using C4:5

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 11, Issue 3, 1997, Pages 405-415

  Ling, Charles X ^a   Parry, John J ^a   Wang, Handong ^a  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

Decision trees; Inductive learning from examples; Nearest neighbor learning algorithms

Indexed keywords

EID: 5844339956     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001497000184     Document Type: Article

References (22)

1. D. W. Aha, "Incremental, instance-based learning of independent and graded concept descriptions", Proc. 1989 Int. Workshop on Machine Learning, Morgan Kaufmann, Irvine, CA, 1989, pp. 387-391.
2. D. W. Aha and R. L. Bankert, "Feature selection for case-based classification of cloud types: An empirical comparison", Proc. AAAI-94 Workshop on Case-Based Reasoning, ed. D. W. Aha, AAAI Press, Menlo Park, CA (Technical Report WS-94-01), 1994, pp. 106-112.
3. D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms", Machine Learning 6 (1991) 37-66.
4. C. Cardie, "Using decision trees to improve case-based learning", Proc. 1993 Int. Conf. on Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993, pp. 25-32.
5. R. Caruana and D. Freitag, "Greedy attribute selection", Proc. 1994 Int. Conf. on Machine Learning, Morgan Kaufmann, CA, 1994, pp. 28-36.
6. S. Cost and S. Salzberg, "A weighted nearest neighbor algorithm for learning with symbolic features", Machine Learning 10 (1993) 57-78.
7. T. Cover, "Estimation by the nearest neighbor rule", IEEE Trans, on Information Theory 14 (1968) 50-55.
8. T. Cover and P. Hart, "Nearest neighbor pattern classification", IEEE Trans. on Information Theory 13 (1967) 21-27.
9. W. Daelemans, S. Gillis, G. Durieux, and A. van den Bosch, "Learnability and markedness in data-driven acquisition of stress", ITK Research Report No. 43, Inst. for Language Tech. and AI (ITK), Tilburg University, 1993.
10. B. Dasarathy, Nearest neighbor (NN) norms: NN pattern classification techniques, IEEE Computer Society Press, Los Alamitos, CA, 1993.
11. J. D. Kelly and L. Davis, "A hybrid algorithm for classification", Proc. IJCAI-91 (13th Int. Conf. on Artificial Intelligence), eds. J. Mylopoulos and R. Reite, Morgan Kaufmann, San Mateo, CA, 1991, pp. 645-650.
12. D. Kibler and D. W. Aha, "Learning representative exemplars of concepts: An initial case study", Proc. 1987 Int. Workshop on Machine Learning, Morgan Kaufmann, Irvine, CA, 1987, pp. 24-30.
13. C. X. Ling and H. Wang, "Computing optimal attribute weight settings for nearest neighbor algorithms", Artificial Intelligence Review 11 (1997) 255-272.
14. M. Mohri and H. Tanaka, "An optimal weighting criterion of case indexing for both numeric and symbolic attributes", Case-Based Reasoning: Papers from the 1994 Workshop, WS-94-01, AAAI Press, Menlo Park, CA, 1994.
15. P. M. Murphy and D. W. Aha, "UCI Repository of machine learning databases [Machine-readable data repository]", Univ. of California, Dept. of Information and Computer Science, Irvine, CA, 1992.
16. J. J. Parry, "Algorithms to calculate weights for the nearest neighbor learning", Master's thesis, Department of Computer Science, University of Western Ontario, 1994.
17. J. Quinlan, "Induction of decision trees", Machine Learning 1, 1 (1986) 81-106.
18. J. Quinlan, C4-5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
19. C. Schaffer, "A conservation law for generalization performance", Proc. 1994 Int. Conf. on Machine Learning, Morgan Kaufmann, CA, 1994.
20. D. Skalak, "Prototype and feature selection by sampling and random mutation hill climbing algorithms", Proc. 1994 Int. Conf. on Machine Learning, Morgan Kaufmann, CA, 1994, pp. 293-301.
21. C. Stanfill and D. Waltz, "Toward memory-based reasoning", Commun. ACM 29 (1986) 1213-1228.
22. D. Wettschereck and T. Dietterich, "An experimental comparison of the nearest neighbor and nearest hyperrectangle algorithms", Machine Learning 19 (1995) 5-28.