%0 Journal Article
%T Random Forests: An Important Feature genes Selection Method of Tumor<br>随机森林：一种重要的肿瘤特征基因选择法
%A 李建更
%A 高志坤
%J 生物物理学报
%D 2009
%I 
%X Feature selection techniques have been widely applied to bioinformatics, where random forests (RF) is an important one. To prove the advantage of RF, significance analysis of microarray (SAM) and ReliefF were employed to compare with it. Support Vectors Machine (SVM) was used to test the feature genes selected by the three methods. The comparison results show that feature genes of RF contain more classification information and can get higher accuracy rate when were applied to classification. As a reliable method, RF should be applied in bioinformatics broadly.
%K Tumor
%K Feature selection
%K Random forests
%K Significance analysis of microarray
%K ReliefF<br>肿瘤
%K 特征选择
%K 随机森林
%K SAM
%K ReliefF
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=6C1554A37E507C0ED91CC80ADF5A4B8D&yid=DE12191FBD62783C&vid=C5154311167311FE&iid=CA4FD0336C81A37A&sid=987EDA49D8A7A635&eid=014B591DF029732F&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=0&reference_num=0