Optimized Kernel Machines for Cancer Classification Using Gene Expression Data

The cancer classification using gene expression data has shown to be very useful for cancer diagnose and prediction. However, the nature of very high dimensionality and relatively small sample size associated with the gene expression data make the tasks of classification quite challenging. In this paper, we present a new approach, which is based on optimizing the kernel function, to improve the performances of the classifiers in classifying gene expression data. Aiming to increase the class separability of the data, we utilize a more flexible kernel function model, the data-dependent kernel, as the objective kernel to be optimized. The experimental results show that using the optimized kernel usually results in a substantial improvement for the K-nearest-neighbor (KNN) algorithm in classifying gene expression data.