摘要
对于许多模式识别问题来说,特征选择是一个非常重要的数据预处理技术,这对于维数高,而样本又相对较小的微阵列数据来说更是如此。提出一种将粒计算与传统的SVM-RFE算法相结合的特征选择算法。这种算法能够有效地去除大部分与分类无关的基因;并且能够搜索到基因数量相对较少而分类能力相对较强的信息基因子集。
Feature selection is an important preprocessing technique for many pattern recognition problems. When the number of features is very large while the number of samples is relatively small as in the microarray data analysis, feature selection is even more important. A feature selection algorithm based on a granular computing and SVM-RFE hybrid algorithm can effectively eliminate most of the irrelevant genes, and can find a more informative gene subset in which the number of informative genes is almost least but its classification performance is almost highest.
出处
《科学技术与工程》
2009年第6期1424-1427,1455,共5页
Science Technology and Engineering
基金
国家自然科学基金(60773172)资助
