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This chapter is devoted to a description of the postsupervised classifier design using fuzzy clustering. We will first derive a modified fuzzy c-means clustering algorithm by slightly generalizing the objective function and introducing some simplifications. The k-harmonic means clustering [177, 178, 179, 119] is reviewed from the point of view of fuzzy c-means. In the algorithm derived from the iteratively reweighted least square technique (IRLS), membership functions are variously chosen and parameterized. Experiments on several well-known benchmark data sets show that the classifier using a newly defined membership function outperforms well-established methods, i.e., the support vector machine (SVM), the k-nearest neighbor classier (k-NN) and the learning vector quantization (LVQ). Also concerning storage requirements and classification speed, the classifier with modified FCM improves the performance and efficiency.
Keywords
- Support Vector Machine
- Membership Function
- Gaussian Mixture Model
- Mahalanobis Distance
- Learning Vector Quantization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2008 Springer-Verlag Berlin Heidelberg
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Miyamoto, S., Ichihashi, H., Honda, K. (2008). Application to Classifier Design. In: Algorithms for Fuzzy Clustering. Studies in Fuzziness and Soft Computing, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78737-2_6
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DOI: https://doi.org/10.1007/978-3-540-78737-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78736-5
Online ISBN: 978-3-540-78737-2
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