An efficient design of a nearest neighbor classifier for various-scale problems

Pattern Recognition Letters

Volumn 31, Issue 9, 2010, Pages 1020-1027

  Lee, Heesung ^a   Hong, Sungjun ^a   Nizami, Imran Fareed ^a   Kim, Euntai ^a  

^a Yonsei University   (South Korea)

Author keywords

Editing for the NN; Feature selection; Genetic algorithm; Nearest neighbor

Indexed keywords

CLASSIFICATION PERFORMANCE; COMPUTATIONAL TIME AND MEMORY; CONVENTIONAL METHODS; EDITING FOR THE NN; EFFICIENT DESIGNS; FEATURE SELECTION; GENETIC ENCODING; NEAREST NEIGHBOR CLASSIFIER; NEAREST NEIGHBORS; REFERENCE SET;

CLASSIFIERS; COMPUTATIONAL EFFICIENCY; GENETIC ALGORITHMS;

FEATURE EXTRACTION;

EID: 77951111106     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2010.01.001     Document Type: Article

References (22)

1. Chen J., Chen H., and Ho S. Design of nearest neighbor classifiers: Multi-objective approach. Internat. J. Approx. Reason. 40 (2005) 3-22
2. Cover T., and Hart P. Nearest neighbor pattern classification. IEEE Trans. Information Theory 13 January (1967) 21-27
3. Davis L. Handbook of Genetic Algorithms (1991), Van Nostrand Reinhold
4. Duda R., Hart P., and Stork D. Pattern Classification (2001), Addison Wesley
5. Gil-pita R., and Yao X. Using a genetic algorithm for editing k-nearest neighbor classifiers. Lect. Notes Comput. Sci. 4881 (2007) 1141-1150
6. Golub T., et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286 October (1999) 531-537
7. Gose E., Johnsonbaugh R., and Jost S. Patten Recognition and Image Analysis (1996), Prentice Hall PTR
8. Hagan M.T., Demuth H.B., and Beale H. Neural Network Design (1995), PWS Publishing Company, USA
9. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer
10. Ho S., Liu C., and Liu S. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Lett. 23 (2002) 1495-1503
11. Hong J., and Cho S. Efficient huge-scale feature selection with speciated genetic algorithm. Pattern Recognition Lett. 27 (2006) 143-150
12. Hwang, J., Rou, K., Park, S., Kim, E., Kang, H., June 2006. PCA based vehicle detection for ACC. In: Proc. 8th Internat. Conf. Elect. Inform. Comm., pp. 98-101.
13. Jain A., and Zongker D. Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Machine Intell. 19 February (1997) 153-158
14. Juo H., and Chang H. A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems. Artificial Intelligence 17 (2004) 919-930
15. Kim E., and Park S. Automatic video segmentation using genetic algorithms. Pattern Recognition Lett. 27 11 (2006) 1252-1265
16. Kuncheva L., and Jain L. Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognition Lett. 20 (1999) 1149-1156
17. Lee H., Hong S., and Kim E. A new genetic feature selection with neural network ensemble. Int. J. Comput. Math. 86 7 (2009) 1105-1117
18. Lee H., and Kim E. A symbiotic evolutionary design of error-correcting code with minimal power consumption. ETRI J. 30 6 (2008) 799-806
19. Lee H., Kim E., and Park M. A genetic feature weighting scheme for pattern recognition. Integrated Comput. Aided Eng. 14 (2007) 161-171
20. Lossos L., et al. Ongoing immunoglobulin somatic mutation in germinal center B cell-like but not in activated B cell-like diffuse large cell lymphomas. Proc. Natl. Acad. Sci. 97 August (2000) 10209-10213
21. Merelo J.J., and Prieto A. G-lvq, a combination of genetic algorithms and lvq. In: Steele N.C., Pearson D.W., and Albrecht R.F. (Eds). Artificial Neural Nets and Genetic Algorithms (1995), Springer-Verlag, New York
22. Murphy, P.M., Aha, D.W., 1994. UCI Repository for machine learning databases. Technical Report, Dept. of Information and Computer Science, Univ. of California, Irvine, Calif..