Abstract
Fuzzy classification proposes an approach to solve uncertainty problem in classification tasks. It assigns an instance to more than one class with different degrees instead of a definite class by crisp classification. This paper studies the usage of fuzzy strategy in classification. Two fuzzy algorithms for sequential self-organizing map and learning vector quantization are proposed based on fuzzy projection and learning rules. The derived classifiers are able to provide fuzzy classes when classifying new data. Experiments show the effectiveness of proposed algorithms in terms of classification accuracy.
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References
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Bezdek, J.C., Pal, N.R.: Two soft relative of learning vector quantization. Neural Networks 8(5), 729–743 (1995)
Karayiannis, N.B., Pai, P.-I.: Fuzzy algorithms for learning vector quantization: generalizations and extensions. In: Rogers, S.K. (ed.) Applications and Science of Artificial Neural Networks, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA. Proceedings of SPIE, vol. 2492, pp. 264–275 (1995)
Keller, J.M., Gary, M.R., Givens, J.A.: A fuzzy k-nearest neighnor algorithm. IEEE Trans. on Systems, Man, and Cybernetics 15(4), 580–585 (1985)
Kohonen, T.: Self-organizing maps, 2nd edn. Springer, Berlin (1997)
Sohn, S., Dagli, C.H.: Self-organizing map with fuzzy class memberships. In: Proceedings of SPIE International Symposium on AreoSense, vol. 4390, 150–157 (2001)
Solaiman, B., Mouchot, M.C., Maillard, E.P.: A hybrid algorithm (HLVQ) combining unsupervised and supervised learning approaches. In: Proceedings of IEEE International Conference on Neural Networks(ICNN), Orlando, USA, pp. 1772–1778 (1994)
Laboratory of computer and information sciences & Neural networks research center, Helsinki University of Technology: SOM Toolbox 2.0., http://www.cis.hut.fi/projects/somtoolbox/
Visa, A., Valkealahti, K., Iivarinen, J., Simula, O.: Experiences from operational cloud classifier based on self-organising map. In: Procedings of SPIE, Orlando, Florida, Applications of Artificial Neural Networks V, vol. 2243, pp. 484–495 (1994)
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Chen, N. (2004). Fuzzy Classification Using Self-Organizing Map and Learning Vector Quantization. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_5
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DOI: https://doi.org/10.1007/978-3-540-30537-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23987-1
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