Title :
Multiple SVM-RFE for multi-class gene selection on DNA Microarray data
Author :
Li Zhang; Xiaojuan Huang
Author_Institution :
School of Computer Science and Technology &
fDate :
7/1/2015 12:00:00 AM
Abstract :
This paper presents a new multi-class gene selection and classification method based on multiple support vector machine recursive feature elimination (SVM-RFE). For a multi-class DNA microarray problem, we solve it as multiple binary classification problems. First, the one-versus-all method is used to decompose the multi-class task into multiple binary problems. Second, an SVM-RFE is adopted to select genes for each binary problem. Then, an SVM classifier is used to train the selected gene data for a binary problem. Finally, we combine the outputs of multiple SVM classifiers. Experimental results on three DNA Microarray datasets show that the proposed method achieves higher classification accuracy.
Keywords :
"DNA","Lungs","Bioinformatics"
Conference_Titel :
Neural Networks (IJCNN), 2015 International Joint Conference on
Electronic_ISBN :
2161-4407
DOI :
10.1109/IJCNN.2015.7280417