Research of Marker Gene Selection for Tumor Classfication Based on Decision Forests

Feature selection techniques have been widely applied to bioinformatics, where decision forests (DF) is an important one. To prove the advantage of DF, Significance Analysis of Microarray (SAM), PCA and ReliefF were employed to compare with it. Support Vectors Machine (SVM) was used to test the feature genes selected by the four methods. The comparison results show that feature genes selected by DF contain more classification information and can get higher accuracy rate when were applied to classification. As a reliable method, DF should be applied in bioinformatics broadly.