Fitness functions in editing k-NN reference set by genetic algorithms

Pattern Recognition

Volumn 30, Issue 6, 1997, Pages 1041-1049

  Kuncheva, Ludmila I ^a,b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b BULGARIAN ACADEMY OF SCIENCES   (Bulgaria)

Author keywords

Editing strategies; Fitness functions; Genetic algorithms; k Nearest Neighbors (k NN) rule

Indexed keywords

DATA REDUCTION; GENETIC ALGORITHMS; MEDICAL COMPUTING; SET THEORY;

FITNESS FUNCTIONS; K-NEAREST NEIGHBORS (KNN) RULE;

OBJECT RECOGNITION;

EID: 0031168619     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(96)00134-3     Document Type: Article

References (16)

1. B. V. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, California (1990).
2. B. V. Dasarathy, Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design, IEEE Trans. Systems Man Cybernet. 24, 511-517 (1994).
3. P. A. Devijver and J. Kittler, On the edited nearest neighbor rule, Proc. 5th Int. Conf. on Pattern Recognition, pp. 72-80 (1980).
4. P. A. Devijver and J. Kittler, Pattern Recognition. A Statistical Approach. Prentice-Hall, Englewood Cliffs, New Jersey (1982).
5. D. L. Wilson, Asymptotic properties of nearest neighbor rules using edited data, IEEE Trans. Systems Man Cybernet. SMC-2, 408-421 (1972).
6. L. I. Kuncheva, Selection of a k-NN reference set by genetic algorithm and index of fuzziness, Proc. Second European Conf. on Fuzzy and Intelligent Technologies, EUFIT'94, Aachen, Germany, pp. 640-644 (1994).
7. L. I. Kuncheva, Editing for the k-nearest neighbors rule by a genetic algorithm, Pattern Recognition Lett. 16, 809-814 (1995).
8. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Massachusetts (1989).
9. S. J. Raudys and A. K. Jain, Small sample size effects in statistical pattern recognition: Recommendations for practitioners and open problems, Proc. 10th Int. Conf. on Pattern Recognition, Atlantic City, New Jersey, pp. 417-423 (1990).
10. G. T. Toussaint, Bibliography on estimation of misclassification, IEEE Trans. Inform. Theory 20, 472-479 (1974).
11. J. Kittler, On the efficiency of data utilization in system error estimation, Proc. 6th Int. Conf. on Pattern Recognition, Munich, Germany, pp. 868-871 (1982).
12. D. S. Moore, S. J. Whitsitt and D. A. Landgrebe, Variance comparisons for unbiased estimators of probability of correct classification, IEEE Trans. Inform. Theory 22, 102-105 (1976).
13. N. Glick, Additive estimators for probabilities of correct classification, Pattern Recognition 10, 211-222 (1978).
14. G. E. Tutz, Smoothed additive estimators for non-error rates in multiple discriminant analysis, Pattern Recognition 18, 151-159 (1985).
15. P. A. Devijver, A multiclass k-NN approach to Bayes risk estimation, Pattern Recognition Lett. 3, 1-6 (1985).
16. L. Prechelt, PROBEN1 - A set of neural network benchmark problems and benchmarking rules, Technical Report, # 21/94 (1994) (1985).