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 ens...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.展开更多
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)+2 种基金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.
文摘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.
