Semi-supervised Clustering in Fuzzy Rule Generation

• Priscilla A. Lopes UFSCar
• Heloisa A. Camargo UFSCar

Resumo

Inductive learning approaches traditionally categorized as supervised, which use labeled data sets, and unsupervised, which use unlabeled data sets in learning tasks. The great volume of available data and the cost involved in manual labeling has motivated the investigation of different solutions for machine learning tasks related to unlabeled data. The approach proposed here fits into this context: a semi-supervised clustering algorithm is applied to a partially labeled data set; the obtained results are used to automatically label the remaining data in the set; following, a supervised learning algorithm is used to generate fuzzy rules from the labeled data. The experiments show that this may be a promising solution for tasks that have encountered difficulties due to partially labeled data.

Referências

Alcala-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., and Herrera, F. (2009). Keel: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing, 13(3):307–318.

Babuska, R., Jager, R., and Verbruggen, H. (1994). Interpolation issues in sugeno-takagi reasoning. In Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on, pages 859–863.

Basu, S., Banerjee, A., and Mooney, R. J. (2002). Semi-supervised clustering by seeding. In ICML ’02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 27–34.

Basu, S., Banerjee, A., and Mooney, R. J. (2004). Active semi-supervision for pairwise constrained clustering. In In Proceedings of the 2004 SIAM International Conference on Data Mining, pages 333–344.

Bensaid, A. M., Hall, L. O., Bezdek, J. C., and Clarke, L. P. (1996). Partially supervised clustering for image segmentation. Pattern Recognition, 29(5):859–871.

Carvalho, F. A. T., Tenorio, C. P., and Junior, N. L. C. (2006). Partitional fuzzy clustering methods based on adaptive quadratic distances. Fuzzy Sets and Systems, 157(21):2833–2857.

Casillas, J. and Carse, B. (2009). Special issue on ”genetic fuzzy systems: Recent developments and future directions”. Soft Comput., 13(5):417–418.

Casillas, J., Herrera, F., Pérez, R., del Jesús, M. J., and Villar, P. (2007). Special issue on genetic fuzzy systems and the interpretability-accuracy trade-off. Int. J. Approx. Reasoning, 44(1):1–3.

Chapelle, O., Schölkopf, B., and Zien, A. (2006). Semi-Supervised Learning. MIT Press, Cambridge, MA.

Chi, Z., Yan, H., and Pham, T. (1996). Fuzzy Algorithms: With Applications To Image Processing and Pattern Recognition. World Scientific.

Cordón, O., del Jesus, M., and Herrera, F. (1999). A proposal on reasoning methods in fuzzy rule-based classification systems. International Journal of Approximate, 20(1):21–45.

Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., and Magdalena, L. (2004a). Special issue on genetic fuzzy systems. Fuzzy Sets and Systems, 141.

Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., and Magdalena, L. (2004b). Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems, 141(1):5–31.

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 7:1–30.

Frank, A. and Asuncion, A. (2010). UCI machine learning repository. [link].

Geng, C., Yuquan, Z., Jianing, T., and Tianhan, H. (2009). An algorithm of semi-supervised web-page classification based on fuzzy clustering. In Information Technology and Applications, 2009. IFITA ’09. International Forum on, volume 1, pages 3–7.

González, A. and Perez, R. (2001). Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems and Man and and Cybernetics and Part B: Cybernetics, 31(3):417–425.

Grira, N., Crucianu, M., and Boujemaa, N. (2005). Active semi-supervised fuzzy clustering for image database categorization. In MIR ’05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, pages 9–16. ACM.

Grira, N., Crucianu, M., and Boujemaa, N. (2006). Fuzzy clustering with pairwise constraints for knowledge-driven image categorisation. Vision, Image and Signal Processing, IEEE Proceedings -, 153(3):299–304.

Grira, N., Crucianu, M., and Boujemaa, N. (2008). Active semi-supervised fuzzy clustering. Pattern Recognition, 41(5):1851–1861.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1):10–18.

Herrera, F. (2008). Genetic fuzzy systems: Taxonomy and current research trends and prospects. Evolutionary Intelligence, 1:27–46.

Hullermeier, E. (2005). Experience-based decision making: A satisficing decision tree approach. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 35(5):641–653.

Ishibuchi, H., Yamamoto, T., and Nakashima, T. (2005). Hybridization of fuzzy gbml approaches for pattern classification problems. IEEE Transactions on Systems and Man and Cybernetics - Part B: Cybernetics, 35(2):359–365.

Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. Matlab Curriculum Series. Prentice Hall.

Janikow, C. Z. (1998). Fuzzy decision trees: issues and methods. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 28(1):1–14.

Juang, C.-F. (2005). Combination of online clustering and q-value based ga for reinforcement fuzzy system design. Fuzzy Systems, IEEE Transactions on, 13(3):289–302.

Klose, A. and Kruse, R. (2005). Semi-supervised learning in knowledge discovery. Fuzzy Sets and Systems, 149:209–233.

Lee, H. E., Park, K. H., and Bien, Z. Z. (2008). Iterative fuzzy clustering algorithm with supervision to construct probabilistic fuzzy rule base from numerical data. IEEE T. Fuzzy Systems, 16(1):263–277.

Liao, T. W., Celmins, A. K., and II, R. J. H. (2003). A fuzzy c-means variant for the generation of fuzzy term sets. Fuzzy Sets and Systems, 135(2):241–257.

Liu, H. and Huang, S. T. (2003). A genetic semi-supervised fuzzy clustering approach to text classification. In In Proceedings of the 4th International Conference on Advances in Web-Age Information Management (WAIMâ03), Lecture Notes in Computer Science, pages 173–180.

Mansoori, E., Zolghadri, M., and Katebi, S. (2008). Sgerd: A steady-state genetic algorithm for extracting fuzzy classification rules from data. IEEE Transactions on Fuzzy Systems, 16(4):1061–1071.

Nomura, H., Hayashi, I., and Wakami, N. (1992). A learning method of fuzzy inference rules by descent method. In Proc. IEEE Int Fuzzy Systems Conf, pages 203–210.

Pedrycz, W., Amato, A., Di Lecce, V., and Piuri, V. (2008). Fuzzy clustering with partial supervision in organization and classification of digital images. 16(4):1008–1026.

Pedrycz, W. and Waletzky, J. (1997). Fuzzy clustering with partial supervision. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 27(5):787–795.

Saez, Y., Zazo, F., and Isasi, P. (2008). A study of the effects of clustering and local search on radio network design: Evolutionary computation approaches. In Hybrid Intelligent Systems, 2008. HIS ’08. Eighth International Conference on, pages 951–954.

Sugeno, M. and Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. Fuzzy Systems, IEEE Transactions on, 1(1):7.

Wagstaff, K., Cardie, C., Rogers, S., and Schrödl, S. (2001). Constrained k-means clustering with background knowledge. In ICML ’01: Proceedings of the Eighteenth International Conference on Machine Learning, pages 577–584. Morgan Kaufmann Publishers Inc.

Yager, R. (1993). On a hierarchical structure for fuzzy modeling and control. Systems, Man and Cybernetics, IEEE Transactions on, 23:1189–1197.

Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.