Feature genes selection and classification with SVM for microarray data of lung tissue

Microarray analysis had been became a widely used tool for disease detection. It used tens of thousands of genes that would be a huge computational problem. The proposed approach applied feature genes selection and classification with support vector machine (SVM) for the microarray data of lung tissue. Based on the proposed approach, feature genes could be finding out according to the epsilon-support vector regression (epsilon-SVR) and selection ranked genes from each class. Moreover, applied multi-class support vector classification (multi-class SVC), cross-validation and parameter search methods to acquire great prediction classification accuracy and less computing time. That is, the effective dimension reduction for finding out feature genes is an important process in the proposed approach. The results shows the feature genes which our purposed approach finding out could acquire great prediction classification accuracy.