On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures

Pattern Recognition

Volumn 42, Issue 11, 2009, Pages 2695-2704

  Kim, Sang Woon ^a   Oommen, B John ^b,c  

^a Myong Ji University   (South Korea)

^b CARLETON UNIVERSITY   (Canada)

^c UNIVERSITY OF AGDER   (Norway)

Author keywords

Class overlapping; Data complexity; k nearest neighbor (k NN) classifier; Prototype reduction schemes (PRS),

Indexed keywords

CLASS-OVERLAPPING; CLASSIFICATION ACCURACY; CONDITIONAL DISTRIBUTION; CORRESPONDING MEASURES; DATA COMPLEXITY; DATA POINTS; ERROR RATE; K-NEAREST NEIGHBOR (K-NN) CLASSIFIER; MISCLASSIFICATION; ORDERS OF MAGNITUDE; OVERLAP MEASURES; PROTOTYPE REDUCTION SCHEMES; PROTOTYPE REDUCTION SCHEMES (PRS),; REAL LIFE DATASETS; SAMPLE POINT; TRAINING SETS;

CLASSIFIERS; LEARNING SYSTEMS; PATTERN RECOGNITION;

DATA REDUCTION;

EID: 67649404578     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.04.019     Document Type: Article

References (29)

1. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-22 1 (2000) 4-37
2. G.E. Batista, R.C. Prati, M.C. Monard1, Balancing strategies and class overlapping, in: 6th International Symposium on Intelligent Data Analysis (IDA 2005) vol. LNCS 3646 (2005) 24-35.
3. Bezdek J.C., and Kuncheva L.I. Nearest prototype classifier designs: an experimental study. International Journal of Intelligent Systems 16 12 (2001) 1445-11473
4. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
5. Chang C.L. Finding prototypes for nearest neighbor classifiers. IEEE Transactions on Computers C-23 11 (1974) 1179-1184
6. Dasarathy B.V. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (1991), IEEE Computer Society Press, Los Alamitos
7. Devijver P.A., and Kittler J. On the edited nearest neighbor rule. Proceedings of 5th International Conference on Pattern Recognition (December 1980) 72-80
8. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press, San Diego
9. Fukunaga K., and Mantock J.M. Nonparametric data reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6 1 (1984) 115-118
10. Hart P.E. The condensed nearest neighbor rule. IEEE Transactions on Information Theory IT-14 (1968) 515-516
11. Ho T.K., and Basu M. Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-24 3 (2002) 289-300
12. Hoekstra A., and Duin R.P.W. On the nonlinearity of pattern classifiers. 13th International Conference on Pattern Recognition (ICPR 1996) (1996) 271-275
13. Kim S.-W., and Oommen B.J. Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognition 36 5 (2003) 1083-1093
14. Kim S.-W., and Oommen B.J. A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Analysis and Applications Journal 6 3 (2003) 232-244
15. Kim S.-W., and Oommen B.J. Enhancing prototype reduction schemes with recursion: a method applicable for "large" datasets. IEEE Transactions on Systems, Man, and Cybernetics-Part B SMC-34 3 (2004) 1384-1397
16. Kim S.-W., and Oommen B.J. On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. Pattern Recognition 37 2 (2004) 227-239
17. Kim S.-W., and Oommen B.J. On using prototype reduction schemes and classifier fusion strategies to optimize kernel-based nonlinear subspace methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 3 (2005) 455-460
18. Kim S.-W., and Oommen B.J. On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognition 40 (2007) 2946-2957
19. Kim S.-W., and Oommen B.J. On using prototype reduction schemes to optimize kernel-based Fisher discriminant analysis. IEEE Transactions on Systems, Man and Cybernetics-Part B 38 2 (2008) 564-570
20. Mansilla E.B., and Ho T.K. On classifier domains of competence. 17th International Conference on Pattern Recognition (ICPR 2004) (2004) 136-139
21. C.L. Blake, C.J. Merz, UCL Machine Learning Databases, University of California, Department of Information and Computer Science, Irvine, CA. Can also be downloaded (as of June 2005) from 〈http://www.ics.uci.edu/mlearn/MLRepository.html〉.
22. Maeda E., and Murase H. Multi-category classification by kernel based nonlinear subspace method. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 99) (1999), IEEE Press, New York
23. R.A. Mollineda, J.S. Sanchez, J.M. Sotoca, Data characterization for effective prototype selection, in: 2nd Iberian Conference on Pattern Recognition and Image Analysis, vol. LNCS-3523, 2005, pp. 27-34.
24. Ritter G.L., Woodruff H.B., Lowry S.R., and Isenhour T.L. An algorithm for a selective nearest neighbor rule. IEEE Transactions on Information Theory IT-21 (1975) 665-669
25. Singh S. PRISM: a novel framework for pattern recognition. Pattern Analysis and Applications 6 (2003) 134-149
26. Sohn S.-Y. Meta analysis of classification algorithms for pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-21 11 (1999) 1137-1144
27. Sotoca J.M., Mollineda R.A., and Sanchez J.S. A meta-learning framework for pattern classification by means of data complexity measures. Revista Iberoamericana de Inteligencia Artificial 10 29 (2006) 31-38
28. Tomek I. Two modifcations of CNN. IEEE Transactions on System, Man and Cybernetics SMC-6 6 (1976) 769-772
29. Xie Q., Laszlo C.A., and Ward R.K. Vector quantization techniques for nonparametric classifier design. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-15 12 (1993) 1326-1330