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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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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