Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network
- 82 Downloads
In this paper two agglomerative learning algorithms based on new similarity measures defined for hyperbox fuzzy sets are proposed. They are presented in a context of clustering and classification problems tackled using a general fuzzy min-max (GFMM) neural network. The proposed agglomerative schemes have shown robust behaviour in presence of noise and outliers and insensitivity to the order of training patterns presentation. The emphasis is also put on the complimentary features to the previously presented incremental learning scheme more suitable for on-line adaptation and dealing with large training data sets. The performance and other properties of the agglomerative schemes are illustrated using a number of artificial and real-world data sets.
Unable to display preview. Download preview PDF.
- 2.S. Mitra and K. Pal, “Self-Organizing Neural Network As a Fuzzy Classifier,” IEEE Trans. on Systems, Man and Cybernetics, vol. 24,no. 3, 1994.Google Scholar
- 3.W. Pedrycz, “Fuzzy Neural Networks with Reference Neurons as Pattern Classifiers,” IEEE Trans. on Neural Networks, vol. 3,no. 5, 1992.Google Scholar
- 6.R.R. Yager and L.A. Zadeh (Eds.), Fuzzy Sets, Neural Networks, and Soft Computing, Van Nostrand Reinhold, 1994.Google Scholar
- 7.B. Gabrys, “Data Editing for Neuro Fuzzy Classifiers,” in Proceedings of the SOCO'2001 Conference, Paisley, UK, 2001.Google Scholar
- 8.B. Gabrys, “Pattern Classification for Incomplete Data,” in Proceedings of 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies KES'2000, Brighton, vol. 1, 2000, pp. 454-457.Google Scholar
- 14.S. Theodoridis and K. Koutroumbas, Pattern Recognition, San Diego, CA: Academic Press, 1999.Google Scholar
- 17.B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.Google Scholar