摘要
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.
In microarray-based cancer classification, gene selection is an importantissue owing to the large number of variables and small number of samples as well as itsnon-linearity. It is difficult to get satisfying results by using conventional linear statisticalmethods. Recursive feature elimination based on support vector machine (SVM RFE) is an effectivealgorithm for gene selection and cancer classification, which are integrated into a consistentframework. In this paper, we propose a new method to select parameters of the aforementionedalgorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice ofselecting the apparently best parameters by using a genetic algorithm to search for a couple ofoptimal parameter. Fast implementation issues for this method are also discussed for pragmaticreasons. The proposed method was tested on two representative hereditary breast cancer and acuteleukaemia datasets. The experimental results indicate that the proposed method performs well inselecting genes and achieves high classification accuracies with these genes.
基金
Project supported by the National Basic Research Program (973) of China (No. 2002CB312200) and the Center for Bioinformatics Pro-gram Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School, Harvard University, Boston, USA
