Abstract
Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good theoretic foundations and good capability of generalization, it is a big challenging task for the large data sets due to the training complexity, high memory requirements and slow convergence. In this paper, we present a new method, SVM classification based on fuzzy clustering. Before applying SVM we use fuzzy clustering, in this stage the optimal number of clusters are not needed in order to have less computational cost. We only need to partition the training data set briefly. The SVM classification is realized with the center of the groups. Then the de-clustering and SVM classification via reduced data are used. The proposed approach is scalable to large data sets with high classification accuracy and fast convergence speed. Empirical studies show that the proposed approach achieves good performance for large data sets.
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Cervantes, J., Li, X., Yu, W. (2006). Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_54
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DOI: https://doi.org/10.1007/11925231_54
Publisher Name: Springer, Berlin, Heidelberg
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