Title :
Gene expression data analysis using support vector machines
Author :
Chu, Feng ; Wang, Lipo
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
Cancer classification is an important problem both for clinical treatment and for biomedical research. Considering the good performance of support vector machines (SVMs) on solving pattern recognition problems, we use a C-SVM to process the B-cell lymphoma data. The principal components analysis (PCA) is used for gene selection. A voting scheme is used to do multi-group classification by k(k-1) binary SVMs. The classification results show that SVMs are effective tools for this problem.
Keywords :
cancer; data analysis; genetics; patient treatment; pattern classification; principal component analysis; support vector machines; B-cell lymphoma data; SVM; biomedical research; cancer classification; clinical treatment; gene expression data analysis; gene selection; multigroup classification; pattern recognition problems; principal components analysis; support vector machines; Biomedical engineering; Cancer; Data analysis; Electronic mail; Gene expression; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines; Testing;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223764
