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The notion of Multiple Kernel Learning is originally proposed in a binary Support Vector Machine classification [25]. The SVM forms a linear discriminant boundary in kernel space with maximum distance between samples of the two considered classes. Among all linear discriminant boundaries separating the data, also named as hyper-planes, a unique one exists yielding the maximum margin of separation between the classes [31], as depicted in Figure .
Figure Linear Classification example [31]
Since SVMs are large margin classifiers, they have the potential to handle large feature spaces and prevent over-fitting [32]. Therefore, this methodology will be adopted in our study to handle the high dimensionality of the genomic data and perform the classification analysis. By replacing the single kernel with a combination of base kernels, the methodology is switched from the single kernel-based classification to the multiple kernel learning.
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