一种改进的基因微阵列数据分类算法  

An Improved Gene Microarray Data Classification Algorithm

作　　者：马越 刘成忠[1] MA Yue;LIU Cheng-zhong(College of information science and technology,Gansu agricultural university,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,兰州730070

出　　处：《软件》2020年第6期7-11,31,共6页Software

基　　金：甘肃省自然科学基金项目(18JR3RA169);甘肃农业大学农业工程学科建设专项基金(GAU-XKJS-2018-252)。

摘　　要：针对基因微阵列数据具有维数高、样本小、冗余高的特点,为了提高基因分类算法的性能,提出一种基于灰狼优化和支持向量机的分类算法。该算法使用主成分分析法进行数据降维,选取15个相关系数最大的基因探针,利用改进的灰狼优化算法对支持向量机的奖罚因子C与核宽度σ进行参数寻优,并在2组公开的癌症微阵列数据上进行试验。实验的准确率分别为95.24%和94.00%,通过与其它算法进行性能对比,该算法具有高效、精准的分类能力,对临床医学应用有极为重要的参考意义。In order to improve the performance of classification algorithm,a classification algorithm based on Grey Wolf Optimizer(GWO)and Support Vector Machine(SVM)is proposed for the high dimension,small sample and high redundancy of gene microarray data.In this algorithm,Principal Component Analysis(PCA)was used to select 15 gene probes with the largest correlation coefficient.Then,the improved gray Wolf optimization algorithm was used to optimize the parameters of reward and punishment factor C and kernel width of SVM,and two groups of cancer microarray data were tested.The accuracy of the experimental results was 95.24%and 94.00%,respectively.Compared with other algorithms,this algorithm has the ability of efficient and accurate classification,which is of great reference significance for clinical medicine application.

关 键 词：基因微阵列 基因分类 主成分分析法 支持向量机 灰狼优化算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]