Improving the performance of ICA based microarray data prediction models with genetic algorithm

It is a challenging task to diagnose tumor type precisely based on microarray data because the number of variables p (genes) is far larger than that of samples, n. Many independent component analysis (ICA) based models had been proposed to tackle the microarray data classification problem with great success. Although it was pointed out that different independent components (ICs) are of different biological significance, up to now, it is still far from well explored for the problem that how to select proper IC subsets to predict new samples best. We try to improve the performance of ICA based classification models by using proper IC subsets instead of all the ICs. A genetic algorithms (GA) based selection process is proposed in this paper, and the selected IC subset is evaluated by the leave-one-out cross validation (LOOCV) technique. The experimental results demonstrate that our GA based IC selection method can further improve the classification accuracy of the ICA based prediction models.