Abstract
How to design powerful and flexible kernels to improve the system performance is an important topic in kernel based classification. In this paper, we present a new granular kernel method to improve the performance of Support Vector Machines (SVMs). In the system, genetic algorithms (GAs) are used to generate feature granules and optimize them together with fusions and parameters of granular kernels. The new granular kernel method is used for cyclooxygenase-2 inhibitor activity comparison. Experimental results show that the new method can achieve better performance than SVMs with traditional RBF kernels in terms of prediction accuracy.
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Jin, B., Zhang, YQ. (2006). Genetic Granular Kernel Methods for Cyclooxygenase-2 Inhibitor Activity Comparison. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_135
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DOI: https://doi.org/10.1007/11759966_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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