Abstract
Recently, semi-supervised classifier learning has received a lot of attention. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. In this paper we propose two such approaches suited to learn fuzzy if-then classifiers. They are both based on evolutionary algorithms. We observed good performance on artificial and “real world” datasets compared to other algorithms proposed in literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bennett, K.P., Demiriz, A.: Semi-supervised support vector machines. In: Cohn, D.A., Kearns, M.S., Solla, S.A. (eds.) Advances in Neural Information processing Systems, pp. 368–374. MIT Press, Cambridge (1998)
Bensaid, M., Bezdek, J.C.: Semi-Supervised point prototype clustering. Pattern Recognition and Artificial Intelligence 12(5), 625–643 (1998)
Bensaid, M., Hall, L.O., Bezdek, J.C., Clarke, L.P.: Partially supervised clustering for image segmentation. Pattern Recognition 29(5), 859–871 (1996)
Breiman, L., Friedman, J.H., Olshen, J.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)
Carse, B., Fogarty, T.C., Munro, A.: Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets and Systems 80, 273–293 (1996)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)
Demiriz, A., Bennett, K.P., Embrechts, M.J.: Semi-supervised clustering using genetic algorithms. In: Proc. Artificial Neural Networks in Applications (1999)
Fahlman, S.: Fast Learning Variations on Backpropagation: an Empirical Study. In: Proc. Connectionist Models Summer School (1988)
Fung, G., Mangasarian, O.L.: Semi-Supervised Support Vector Machines for Unlabeled Data Classification. Data Mining Institute, TR 99–05 (1999)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins, G. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Francisco (1991)
Holland, J.H.: Adaptation in natural and artificial Systems. The University of Michigan Press, Ann Arbor (1975); reprint by MIT Press (1992)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Waterman, D.A., Hayes-Roth, F. (eds.) Pattern-Directed Inference Systems, pp. 313–329. Academic Press, London (1978)
Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis. Kluwer Verlag, Dordrecht (1998)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice Hall, Englewood Cliffs (1988)
Klose, A., Kruse, R.: Enabling Neuro-Fuzzy Classification to Learn From Partially Labelled Data. In: Proc. FUZZ-IEEE 2002, IEEE, Piscataway (2002)
Labzour, T., Bensaid, A., Bezdek, J.: Improved Semi-Supervised Point- Prototype Clustering Algorithms. In: Proc. of Intl. Conf. on Fuzzy Systems, pp. 1383–1387 (1998)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University of California, Irvine, CA, [Online] Available: http://www.ics.uci.edu/mlearn/MLRepository.html
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine learning 39(2/32), 103–134 (2000)
Nürnberger, A., Klose, A., Kruse, R.: Effects of Antecedent Pruning in Fuzzy Classification Systems. In: Proc. KES 2000, pp. 154–157. IEEE, Piscataway (2000)
Nürnberger, A., Borgelt, C., Klose, A.: Improving naive Bayes classifiers using neuro-fuzzy learning. In: Proc. ICONIP 1999, Perth, Australia, pp. 154–159 (1999)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Pedrycz, W.: Algorithms of fuzzy clustering with partial supervision. Pattern Recognition Letters 3, 13–20 (1985)
Rissanen, J.: A universal prior for integers and estimation by minimum description length. Annals of Statistics 11, 416–431 (1983)
Skabar, A.: Augmenting supervised neural classifier training using a corpus of unlabeled data. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 174–185. Springer, Heidelberg (2002)
Smith, S.F.: A learning system based on genetic adaptive algorithms. PhD thesis, Department of Computer Science, University of Pittsburgh (1980)
Verikas, A., Gelzinis, A., Malmquist, K.: Using Unlabeled Data for Learning Classification Problems. In: Jain, L.C., Kacprzyk, J. (eds.) New Learning Paradigms in Soft Computing, pp. 368–403. Physica, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klose, A. (2003). Approaches to Semi-supervised Learning of Fuzzy Classifiers. In: Günter, A., Kruse, R., Neumann, B. (eds) KI 2003: Advances in Artificial Intelligence. KI 2003. Lecture Notes in Computer Science(), vol 2821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39451-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-39451-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20059-8
Online ISBN: 978-3-540-39451-8
eBook Packages: Springer Book Archive