Mao Yong, Zhou Xiao Bo, Pi Dao Ying, Sun You Xian
National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou 310027, China.
Genomics Proteomics Bioinformatics. 2005 Nov;3(4):238-41. doi: 10.1016/s1672-0229(05)03033-0.
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.
在本研究中，我们提出了一种用于训练协作支持向量机集成（CSVME）的构造性算法。CSVME将集成架构设计与集成中单个支持向量机的协作训练相结合。与大多数先前关于训练集成的研究不同，CSVME既强调准确性，又强调集成中单个支持向量机之间的协作。基于递归分类器消除选择的一组支持向量机用于CSVME，并且通过10折交叉验证确定用于构建CSVME的单个支持向量机的数量。这种支持向量机集成（SVME）已经在先前通过蛋白质组质谱法获得的两个卵巢癌数据集上进行了测试。通过组合多个单个支持向量机，所提出的方法比所有基础支持向量机的支持向量机集成（SVME）具有更好的性能。
