Abstract
Support vector regression (SVR) is a new technique for pattern classification , function approximation and so on. In this paper we propose an new constructing approach of classification rules based on support vector regression and its derivative characteristics for the classification task of data mining. a new measure for determining the importance level of the attributes based on the trained SVR is proposed. Based on this new measure, a new approach for clas-sification rule construction using trained SVR is proposed. The performance of the new approach is demonstrated by several computing cases. The experimen-tal results prove that the approach proposed can improve the validity of the ex-tracted classification rules remarkably compared with other constructing rule approaches, especially for the complicated classification problems.
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Zhang, D., Yang, Z., Fan, Y., Wang, Z. (2007). Constructing Classification Rules Based on SVR and Its Derivative Characteristics. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_28
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DOI: https://doi.org/10.1007/978-3-540-73871-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
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