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Granular Kernel Trees with parallel Genetic Algorithms for drug activity comparisons
by Bo Jin, Yan-Qing Zhang, Binghe Wang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 1, No. 3, 2007
Abstract: With the growing interests of biological data prediction and chemical data prediction, more powerful and flexible kernels need to be designed so that the prior knowledge and relationships within data can be expressed effectively in kernel functions. In this paper, Granular Kernel Trees (GKTs) are proposed and parallel Genetic Algorithms (GAs) are used to optimise the parameters of GKTs. In applications, SVMs with new kernel trees are employed for drug activity comparisons. The experimental results show that GKTs and evolutionary GKTs can achieve better performances than traditional RBF kernels in terms of prediction accuracy.

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