Evolutionary Granular Kernel Trees and Applications in Drug Activity Comparisons

Kernel methods, specifically support vector machines (SVMs), have been widely used in many fields for data classification and pattern recognition. The performance of SVMs is mainly affected by kernel functions. With the growing interest of biological data prediction and chemical data prediction such as structure-property based molecule comparison, protein structure prediction and long DNA sequence comparison, more powerful and flexible kernels need to be designed in order effectively to express the prior knowledge and relationships within each data item. In this paper, the granular kernel concept is presented and related properties are described in detail. A hierarchical kernel design method is proposed to construct granular kernel trees (GKTs). For a particular problem, genetic algorithms (GAs) are used to find the optimum parameter settings of GKTs. In applications, SVMs with new kernel trees are employed for the comparisons of drug activities. The experimental results show that SVMs with GKTs and evolutionary GKTs can achieve better performances than SVMs with traditional RBF kernels in terms of prediction accuracy.