Genetic granular kernel methods for cyclooxygenase-2 inhibitor activity comparison

Authors:
Bo Jin;Yan-Qing Zhang
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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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.