Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling  

作　　者：Yong Mao Xiao-Bo Zhou Dao-Ying Pi You-Xian Sun 

机构地区：[1]National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering,Zhejiang University,Hangzhou 310027, China [2]Harvard Center for Neurodegeneration and Repair, Harvard Medical School and Brigham and Women＇s Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA.

出　　处：《Genomics, Proteomics & Bioinformatics》2005年第4期238-241,共4页基因组蛋白质组与生物信息学报（英文版）

基　　金：partly supported by the National Natural Science Foundation of China (No.60574019 and 60474045);the National Basic Research Program(973 Program)of China(No.2002CB312200);the Key Technologies R&D Program of Zhejiang Province (No.2005C21087);the Academician Foundation of Zhejiang Province(No.2005A1001-13);the Center for Bioinformatics Program Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School,Boston,USA.

摘　　要：In this study, we present a constructive algorithm for training cooperative support vector machine ensembles （CSVMEs）. CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.In this study, we present a constructive algorithm for training cooperative support vector machine ensembles （CSVMEs）. CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

关 键 词：support vector machine ensemble （SVME） design constructive approach proteomic profiling cancer diagnosis 

分 类 号：R73[医药卫生—肿瘤]