Abstract
This paper put forwards a novel support vector machine ensemble construction method based on subtractive clustering analysis. Firstly, the training samples are clustered into several clusters according to their distribution with subtractive clustering algorithm. Then small quantities of representative instances from them are chosen as training subsets to construct support vector machine components. At last, the base classifiers’ outputs are aggregated to obtain the final decision. Experiment results on UCI datasets show that the SVM ensemble generated by our method has higher classification accuracy than Bagging, Adaboost and k-fold cross validation algorithms.
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Wang, C., Yuan, H., Liu, J., Zhou, T., Lu, H. (2007). A Novel Support Vector Machine Ensemble Based on Subtractive Clustering Analysis. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_94
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DOI: https://doi.org/10.1007/978-3-540-71701-0_94
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