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Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.
Rocznik
Tom
Strony
105--111
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
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autor
- Silesian University of Technology, Institute of Electronics, Akademicka St. 16, 44-100 Gliwice, Poland
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Bibliografia
- [1] BEKIOS-CALFA J., BUENAPOSADA J. BAUMELA L., Revisiting linear discriminant techniques in gender recognition, IEEE Trans. Patt. An. Mach. Int. 33, 2011, pp. 858-864.
- [2] BEZDEK J.C., Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum, New York, 1981.
- [3] CECOTTI H., GRASER A., Convolutional neural networks for P300 detection with application to Brain-Computer interface, IEEE Trans Patt. An. Mach. Int. 33, 2011, pp. 433-445.
- [4] CHAOVALITWONGSE W., FAN Y.J., SACHDEO R., On the time series K-nearest neighbor classification of abnormal brain activity, IEEE Trans Sys. Man Cyber. A, 37, 2007, pp. 1005-1016.
- [5] COVER T.M., HART P.E., Nearest neighbor pattern classification, IEEE Trans. Inf. Theory 13, 1967, pp. 21-27.
- [6] DUDA R.O., HART P.E., STORK D.G., Pattern Classification. Wiley-Interscience, New Jersey, 2000.
- [7] HASTIE T., TIBSHIRANI R., Discriminant adaptive nearest neighbor classification, IEEE Trans. Patt. An. Mach. Int. 18, 1996, pp. 607-616.
- [8] JAIN A.K., Data clustering: 50 years beyond K-means, Patt. Rec. Let. 31, 2010, pp. 651-666.
- [9] KAUFMAN L., ROUSSEEUW P., Finding Groups In Data. Wiley-Interscience, New Jersey, 1990.
- [10] LIANG Z., LI Y., SHI P., A note on two-dimensional linear discriminant analysis, Patt. Rec. Let. 29, 2008, pp. 2122-2128.
- [11] MITANI Y., HAMAMOTO Y., A local mean-based nonparametric classifier, Patt. Rec. Let. 27, 2006, pp. 1151-1159.
- [12] PRZYBYLA T., JEZEWSKI J., HOROBA K., ROJ D., Hybrid Fuzzy Clustering Using LP Norms, Intelligent Information and Database Systems, Editors: Ngoc Thanh Nguyen, Chong Gun Kim, Adam Janiak, LNAI 6591/Lecture Notes in Computer Science, Springer Verlag, 2011, pp. 187-196.
- [13] PRZYBYLA T., JEZEWSKI J., ROJ D. On Hybrid Fuzzy Clustering Method, Information Technologies in Biomedicine, Editors: PIETKA E., KAWA J., Advances in Soft Computing Series, Vol. 69, Springer Verlag, 2010, pp. 3-14.
- [14] PRZYBYLA T., JEZEWSKI J., ROJ D. Unsupervised clustering for fetal state assessment based on selected features of the cardiotocographic signals, Journal of Medical Informatics and Technologies, Vol. 13, 2009, pp. 157-162.
- [15] RODRIGUEZ-LUJAN I., SANTA CRUZ S., HUERTA R., On the equivalence of kernel Fisher discriminant analysis and kernel quadratic programming feature selection, Patt. Rec. Let. 32, 2011, pp. 1567-1571.
- [16] SCHOELKOPF B., SMOLA A.J., Learning with Kernels, The MIT Press, 2002.
- [17] SCHEME E.J., ENGLEHART K.B., HUDGINS B.S., Selective classification for improved robustness of meyelectric control under nonideal conditions, IEEE Trans. Patt. An. Mach. Int. 58, 2011, pp. 1698-1705.
- [18] SHAWE-TAYLOR J., CRISTIOANINI N., Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
- [19] ZHENG W., ZHAO L., ZOU C., Locally nearest neighbor classifier for pattern classification, Patt. Rec. 37, 2004, pp. 1307-1309.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0011