NRMCS : Noise removing based on the MCS

Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

Volumn 1, Issue , 2008, Pages 89-93

  Wang, Xi Zhao ^a   Wu, Bo ^a   He, Yu Lin ^a   Pei, Xiang Hao ^a  

^a HEBEI UNIVERSITY   (China)

Author keywords

ICF; MCS; Noise; Representative subset; Sample selection; Wilson Editing

Indexed keywords

CONTROL THEORY; CYBERNETICS; FUSION REACTIONS; INERTIAL CONFINEMENT FUSION; LEARNING SYSTEMS; ROBOT LEARNING;

ICF; MCS; NOISE; REPRESENTATIVE SUBSET; SAMPLE SELECTION; WILSON EDITING;

COMPUTATIONAL METHODS;

EID: 57849141632     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2008.4620384     Document Type: Conference Paper

References (18)

1. B. V. Dasarathy. Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design [J]. IEEE. Trans Syst.man Cybern. March, 1994, 24(3):511-517.
2. P. E. Hart. The condensed nearest neighbor rule [J]. IEEE Trans. Information Theory, May. 1968, IT214(3):515-516.
3. G. W. Gates. The reduced nearest neighbor rale [J]. IEEE Trans. Information Theory, May 1972, IT218(3) :431-433.
4. G. L. Ritter, H. B. Woodruff, S. R. Lowry and T. L. Isenhour, An algorithm for a selective nearest neighbour rule, IEEE Trans. Inform. Theory IT11-IT21 (1975), 665-669.
5. C. W. Swonger, Sample set condensation for a condensednearest neighbor decision rule for pattern recognition, in:S. Watanade (Ed.), Frontiers of Pattern Recognition, Academic Press, New York, 1972, pp. 511-519.
6. J. R. Ullmann, "Automatic selection of reference data for use in a nearest neighbor method of pattern classification," ZEEE Trans. Information Theory, vol. IT-20, no. 4, pp. 541-543, July 1974.
7. I. Tomek, "Two modifications of CNN," IEEE Trans. Syst., Man, Cybern., vol. SMC-6, no. 11, pp. 769-772, Nov. 1976.
8. K. C. Gowda and G. Krishna, "The condensed nearest neighbor rule using the concept of mutual nearest neighborhood," IEEE Trans. Information Theory, vol. IT-25, no. 4, pp. 488490, July 1979.
9. L. I. Kuncheva, J. C. Bezdek, Nearest prototype classification: clustering, genetic algorithms, or random search, IEEE Trans. Syst. Man and Cybern. 28 (1) (1998)160-164
10. V. Cerveron, A. Fuertes, Parallel random search and Tabu search for the minimal consistent subset selection problem, Lecture Notes in Computer Science, Vol. 1518, Springer, Berlin, 1998, pp. 248-259.
11. Henry Brighton, "Advances in Instance Selection for Instance-Based Learning Algorithms" Data Mining and Knowledge Discovery, 6, 153-172, 2002.
12. Zhang, J., Yim, Y.-S., & Yang, J.(1997). Intelligent selection of instances for prediction functions in lazy learning algorithms. Artificial Intelligence Review, 11:1-5, 175-191.
13. Wilson, D. R., Martinez, T. R.(2000). Reduction techniques for instance-based learning algorithms. Machine Learning, 38:3, 257-286.
14. C. L. Chang. Finding prototypes for nearest neighbor classifiers [J]. IEEE Trans. Computers, Nov. 1974, c223(11):1179-1184.
15. Smyth, B. and Keane, M. T. 1995. Remembering to forget. In IJCAI-95, Proceedings of the Fourteenth International Conference on Artificial Intelligence, C. S. Mellish (Ed.), Vol. 1., pp. 377-382.
16. H B Zhang, G Y Sun. Optimal reference subset selection for nearest neighbor classification by tabu search [J]. Pattern Recognition, 2002, 35:1481-1490
17. F Angiulli. Fast Condensed Nearest Neighbor Rule [C]. In: Proc of the 22nd International Conference on Machine Learning, Bonn: ACM Press, 2005. 25-32
18. Wilson, D. L. 1972. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2(3):408-421.