数据采集与处理2018，Vol.33Issue(3)：426-435,10.DOI:10.16337/j.1004-9037.2018.03.005
基于对称不确定性和邻域粗糙集的肿瘤分类信息基因选择
Informative Gene Selection for Tumor Classification Based on Symmetric Uncertainty and Neighborhood Rough Set
摘要
Abstract
Informative gene selection is an essential step to perform tumor classification with large scale gene expression profiles .However ,it is difficult to select informative genes related to tumor from gene expression profiles because of its characteristics such as high dimensionality and relatively small samples , many noises ,and some of the genes are superfluous and irrelevant .To deal with the challenging problem of finding an informative gene subset with the least number of genes but the highest classification per-formance ,a novel hybrid gene selection algorithm named SUNRS is proposed based on the symmetric un-certainty (SU) and neighborhood rough set (NRS) .Firstly ,the symmetric uncertain index ,which aims to eliminate redundant and irrelevant genes ,is used to select top-ranked genes as the candidate gene sub-set .Secondly ,the neighborhood rough set reduction algorithm is used to obtain the target gene subset by optimizing the candidate gene subset .Experimental results show that the proposed algorithm can obtain higher classification accuracy with less informative gene ,which not only improves the generalization per-formance of the algorithm ,but also enhances the time efficiency .关键词
基因表达谱/邻域粗糙集/对称不确定性/特征选择/肿瘤分类Key words
gene expression profiles/neighborhood rough set/symmetric uncertainty/feature selection/tumor classification分类
信息技术与安全科学引用本文复制引用
叶明全,高凌云,伍长荣,黄道斌,胡学钢..基于对称不确定性和邻域粗糙集的肿瘤分类信息基因选择[J].数据采集与处理,2018,33(3):426-435,10.基金项目
国家自然科学基金(61672386)资助项目 （61672386）
安徽省自然科学基金(1708085MF142)资助项目 （1708085MF142）
教育部人文社会科学研究规划基金(16YJAZH071)资助项目 （16YJAZH071）
安徽高校省级自然科学研究重点基金(KJ2014A266 ,KJ2016A275)资助项目 （KJ2014A266 ,KJ2016A275）
安徽高校人文社会科学研究重点基金(SK2016A0953 ,SK2016A0964)资助项目. （SK2016A0953 ,SK2016A0964）
