Genetic Granular Kernel Methods for Cyclooxygenase-2 Inhibitor Activity Comparison

  • Bo Jin
  • Yan-Qing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


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.


Prediction Accuracy Convolution Kernel Training Accuracy String Kernel Support Vector Classification 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Jin
    • 1
  • Yan-Qing Zhang
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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