北京生物医学工程2012，Vol.31Issue(6)：574-578,5.DOI:10.3969/j.issn.1002-3208.2012.06
一种面向小样本数据的错标记样本识别方法
A mislabeled sample recognition method for small sample data
摘要
Abstract
Objective To propose a new method UCL-stability based on the CL-stability method to solve the mislabeled sample problem. Methods According to the number of significant differential features ( after sample label flipping) , UCL-stability proposes a voting weight in order to measure the effects of flipping different samples' label. Results The experimental results of two cancer microarray data sets indicate that both UCL-stability and CL-stability can recognize the suspect samples effectively. The further experiments of artificial mislabeling show that UCL-stability can obtain a higher value of precision and recall. Conclusions The UCL-stability algorithm not only considers the effects of a single sample's mislabeling,but also distinguishes the effects of different samples' mislabeling. In order to measure the effects quantitatively, we employ the feature information and achieve preferable results.关键词
错标记/小样本数据/微阵列Key words
mislabeling/ small sample data/ microarray分类
医药卫生引用本文复制引用
秦瑞斌,郑浩然,周宏..一种面向小样本数据的错标记样本识别方法[J].北京生物医学工程,2012,31(6):574-578,5.基金项目
973计划(2011CB910203)资助 （2011CB910203）
