Abstract
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Experimental results show that S2KFCM can improve classification accuracy significantly, compared with conventional classifiers trained with a small number of labeled data only. Also, it outperforms a similar approach S2FCM.
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References
Saint-Jean, C., Frelicot, C.: A Robust Semi-Supervised EM Clustering Algorithm with a Reject Option. In: Proceedings of the International Conference on Pattern Recogntion (2002)
Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P.: Partial Supervised Clustering for Image Segmentation. Pattern Recognition 29, 859–871 (1996)
Pedrycz, W., Waletzky, J.: Fuzzy Clustering with partial supervision. IEEE Trans. on Systems, Man, and Cybernetics, Part B-Cybernetics 27, 787–795 (1997)
Bennett, K., Demiriz, A.: Semi-Supervised Support Vector Machines. Advances in Neural Information Processing Systems 11, 368–374 (1999)
Zhang, D.Q., Chen, S.C.: Kernel-based Fuzzy and Possibilistic C-Means Clustering. In: Proceedings of the International Conference on Artificial Neural Networks, Istanbul, Turkey, pp. 122–125 (2003)
Zhang, D.Q., Chen, S.C.: A Novel Kernelised Fuzzy C-Means Algorithm with Application in Medical Image Segmentation. Artificial Intelligence in Medicine (2004) (in press)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley, Chichester (2001)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
UCI Repository of Machine Learning Databases. University of California, Irvine, available from http://www.ics.uci.edu/~mlearn/MLRository.html
Chen, S.C., Yang, X.B.: Alternative Linear Discriminant Analysis. Pattern Recognition (2004) (in press)
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhang, D., Tan, K., Chen, S. (2004). Semi-supervised Kernel-Based Fuzzy C-Means. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_191
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DOI: https://doi.org/10.1007/978-3-540-30499-9_191
Publisher Name: Springer, Berlin, Heidelberg
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