A new fast prototype selection method based on clustering

Pattern Analysis and Applications

Volumn 13, Issue 2, 2010, Pages 131-141

  Olvera López, J Arturo ^a   Carrasco Ochoa, J Ariel ^a   Martínez Trinidad, J Francisco ^a  

^a INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

Border prototypes; Clustering; Data reduction; Instance based classifiers; Prototype selection; Supervised classification

Indexed keywords

EID: 77951767444     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-008-0142-x     Document Type: Article

References (30)

1. Kuncheva LI, Bezdek JC (1998) Nearest prototype classification, clustering, genetic algorithms, or random search? IEEE Trans Syst Man Cybern C28(1): 160-164.
2. Bezdek JC, Kuncheva LI (2001) Nearest prototype classifier designs: an experimental study. Int J Intell Syst 16(12): 1445-1473.
3. Wilson DR, Martínez TR (2000) Reduction techniques for instance-based learning algorithms. Mach Learn 38: 257-286.
4. Brighton H, Mellish C (2002) Advances in instance selection for instance-based learning algorithms. Data Min Knowl Disc 6(2): 153-172.
5. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13: 21-27.
6. Atkeson CG, Moorel AW, Schaal S (1997) Locally weighted learning. Artif Intell Rev 11(1-5): 11-73.
7. Vapnik V (1995) The nature of statistical learning theory. Springer, New York.
8. Vapnik VN (1998) Statistical learning theory. Wiley, New York.
9. Cristanni N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge.
10. Quinlan JR (1993) C4. 5: programs for machine learning. Morgan Kaufmann, San Mateo.
11. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York.
12. Hart PE (1968) The condensed nearest neighbor rule. IEEE Trans Inf Theory 14: 515-516.
13. Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 2: 408-421.
14. Chidananda GK, Krishna G (1979) The condensed nearest neighbor rule using the concept of mutual nearest neighborhood. IEEE Trans Inf Theory 25: 488-490.
15. Chien-Hsing C, Bo-Han K, Fu C (2006) The generalized condensed nearest neighbor rule as a data reduction method. In: Proceedings of the 18th international conference on pattern recognition. IEEE Computer Society, Hong-Kong, pp 556-559.
16. Tomek I (1976) An experiment with the edited nearest-neighbor rule. IEEE Trans Syst Man Cybern 6-6: 448-452.
17. Devijver PA, Kittler J (1980) On the edited nearest neighbor rule. In: Proceedings of the fifth international conference on pattern recognition, Los Alamitos, CA, pp 72-80.
18. Liu H, Motoda H (2002) On issues of instance selection. Data Min Knowl Disc 6: 115-130.
19. Spillmann B, Neuhaus M, Bunke H, Pe{ogonek}kalska E, Duin RPW (2006) Transforming strings to vector spaces using prototype selection. In: Yeung D-Y et al (eds) SSPR & SPR 2006, Lecture Notes in Computer Science, vol 4109, Hong-Kong, pp 287-296.
20. Lumini A, Nanni L (2006) A clustering method for automatic biometric template selection. Pattern Recogn 39: 495-497.
21. Venmann CJ, Reinders MJT (2005) The nearest sub-class classifier: a compromise between the nearest mean and nearest neighbor classifier. IEEE Trans Pattern Anal Mach Intell 27(9): 1417-1429.
22. Venmann CJ, Reinders MJT, Backer E (2002) A maximum variance clustering algorithm. IEEE Trans Pattern Anal Mach Intell 24(9): 1273-1280.
23. Mollineda RA, Ferri FJ, Vidal E (2002) An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering. Pattern Recogn 35: 2771-2782.
24. Raicharoen T, Lursinsap C (2005) A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm. Pattern Recognit Lett 26(10): 1554-1567.
25. Karaçali B, Krim H (2002) Fast minimization of structural risk by nearest neighbor rule. IEEE Trans Neural Netw 14: 127-137.
26. Asuncion A, Newman DJ (2007) UCI machine learning repository. In: University of California, School of Information and Computer Science, Irvine, CA. http://www. ics. uci. edu/~mlearn/MLRepository. html.
27. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7): 1895-1924.
28. Vojtech F, Václav H (2004) Statistical pattern recognition toolbox for Matlab. Research report, Center for Machine Perception Department of Cybernetic, Faculty of Electrical Engineering, Czech Technical University.
29. Witten IH, Frank E (2005) Data mining: practical machine learning tools techniques, 2nd edn. Morgan Kaufmann, San Francisco.
30. The MathWorks Inc. (1994-2008) Natick. [http://www. mathworks. com].