基于相交邻域粗糙集的基因微阵列数据分类  

Gene Microarray Data Classification Based on Intersecting Neighborhood Rough Set

作　　者：孟军[1] 李锐[1] 郝涵[1] 

机构地区：[1]大连理工大学计算机科学与技术学院,大连116024

出　　处：《计算机科学》2015年第6期37-40,66,共5页Computer Science

基　　金：辽宁省自然科学基金项目(20130200029)资助

摘　　要：在对基因微阵列数据的特征选择和分类的研究中,粗糙集理论是一个可以消除冗余基因的有效工具。但是传统的粗糙集模型不能很好地处理连续型数值数据,而离散化方法可能会导致信息的丢失。为此,提出了一种基于相交邻域粗糙集模型的属性约简算法,即将传统粗糙集中的距离邻域扩展为相交邻域,采用基于集合的方式来定义近似,以此构建粗糙集模型。在癌症数据集上进行实验,结果表明基于集合近似和相交邻域的粗糙集模型可以取得较好的分类效果,并且通过对选择出的基因进行GO术语分析,进一步证明了该模型的有效性。In the research of gene microarray data classification and feature selection, rough set theory is an effective tool, as it can eliminate redundant genes. However a drawback in traditional rough set is that it cannot handle with con- tinuous numeric data well, and discretization method may lead to the loss of information. We proposed an attribute re- duction algorithm based on intersecting neighborhood rough set, extended the distance neighborhood to intersecting neighborhood and employed the definition of approximation based on set, to build the rough set model. Experimental re- sults on three cancer data sets show that the rough set model based on the set approximate and intersecting neighbor- hood is effective and efficient. Meanwhile, the analysis of GO terms on selected genes further proves the validity of the model.

关 键 词：粗糙集 相交邻域 基因微阵列数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]