一种基于互信息最大化的模型无关基因选择方法  

Model-free Gene Selection Method Based on Maximum Mutual Information

作　　者：魏莎莎[1] 陆慧娟[1] 安春霖[1] 郑恩辉[2] 金伟[1] 

机构地区：[1]中国计量学院信息工程学院,杭州310018 [2]中国计量学院机电工程学院,杭州310018

出　　处：《计算机科学》2014年第9期243-247,共5页Computer Science

基　　金：国家自然科学基金(61272315;60842009;60905034);浙江省自然科学基金(Y1110342;Y1080950);浙江省科技厅国际合作项目(2012C24030)资助

摘　　要：针对大规模基因芯片高维度的基因表达数据存在大量无关和冗余特征可能降低分类器性能的问题,提出了一种基于互信息最大化方法(MMI)和与遗传算法的模型无关的基因选择方法来将特征选择转化为全局优化问题,其中的适应度函数定义为类间距离与类内距离之比,适应程度高。为了评价算法的性能,采用3个数据集进行了实验,结果表明MMIGA-Selection取得了较好的效果,在每个数据集上获得了较高的5折交叉验证正确率。MMIGA-Selection主要有两个优点:一是可以有效减少冗余基因;二是模型无关性,选择得出的特征子集可直接用于其他类型的分类器,分类精度较高。The large number of irrelevant and redundant features in high dimensionality of large-scale gene chip expression data may reduce the performance of the classifiers. We proposed a model-free gene selection method based on the maximum mutual information （MMI） to transform feature selection into a global optimization problem. The fitness function was defined as the distance between the class and class in the ratio of the distance. In order to evaluate the performance of the algorithm, experiments were done in three data sets. Experimental results show that MMIGA-Selection obtains a better effect in every data set of the 5 fold cross validation accuracy. MMIGA-Selection has two main advantages. First, it can effectively reduce the redundant genes. Second, the model-free algorithm makes the feature subset directly apply to other types of classifier and obtains higher classification accuracy.

关 键 词：互信息最大化 模型无关 遗传算法 基因选择 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]