面向高维微阵列数据的集成特征选择算法

J4 ›› 2016, Vol. 38 ›› Issue (07): 1330-1337.

面向高维微阵列数据的集成特征选择算法

孙刚1,2，张靖1,3

（1.合肥工业大学计算机与信息学院,安徽合肥 230009;
2.阜阳师范学院计算机与信息工程学院,安徽阜阳 236037;
3.国网安徽省电力公司信息通信分公司,安徽合肥 230061）

收稿日期:2015-05-25 修回日期:2015-09-01 出版日期:2016-07-25 发布日期:2016-07-25
基金资助:
国家自然科学基金（51174257/F030504）；中央高校基本科研业务费专项资金（2013BHZX0040）；安徽省级科研机构委托专项重点项目（2013WLGH01ZD）

130X.2016.07.005An ensemble feature selection algorithm
for high dimensional microarray data

SUN Gang1,2，ZHANG Jing1,3

(1.School of Computer and Information,Hefei University of Technology,Hefei 230009;
2.School of Computer and Information Engineering,Fuyang Teachers College,Fuyang 236037;
3.State Grid Anhui Information and Telecommunication Company,Hefei 230061,China)

Received:2015-05-25 Revised:2015-09-01 Online:2016-07-25 Published:2016-07-25

摘要/Abstract

摘要：

特征选择算法是微阵列数据分析的重要工具，特征选择算法的分类性能和稳定性对微阵列数据分析至关重要。为了提高特征选择算法的分类性能和稳定性，提出一种面向高维微阵列数据的集成特征选择算法来弥补单个基因子集信息量的不足，提高基因特征选择算法的分类性能和稳定性。该算法首先采用信噪比方法选择若干区分基因；然后对每个区分基因利用条件信息相关系数评估候选基因与区分基因的相关性，生成多个相关基因子集，最后，通过集成学习技术整合多个相似基因子集。实验结果表明，本文提出的集成特征选择算法的分类性能以及稳定性在多数情况下均优于只选择单个基因子集的方法。

关键词: 微阵列数据, 信噪比, 条件相关系数, 特征选择

Abstract:

Feature selection algorithms are an important tool for microarray data analysis, thus their classification ability and stability are essential for data analysis. We propose an ensemble feature selection algorithm for high dimensional microarray data to compensate for the lack of information on a single gene subset. We firstly adopt the signal noise ratio method to select discriminative genes, and then generate relevant gene subsets by evaluating the correlation between the candidate gene and discriminative gene through conditional correlation coefficients. We finally integrate resemblant gene subsets through the ensemble learning technology. Experimental results show that in most cases the classification ability and stability of the proposed algorithm is superior to those that select only a single gene subset.

Key words: microarray data;signal noise ratio;conditional correlation coefficient;feature selection

孙刚1,2，张靖1,3. 面向高维微阵列数据的集成特征选择算法[J]. J4, 2016, 38(07): 1330-1337.

SUN Gang1,2，ZHANG Jing1,3. 130X.2016.07.005An ensemble feature selection algorithm
for high dimensional microarray data [J]. J4, 2016, 38(07): 1330-1337.

参考文献［15］

［1］	Kim Y S,Street W N,Menczer F.Data mining:Opportunities and challenges［M］.Hershey:Idea Group Publishing,2003.
［2］	Saeys Y, Inza I, Larrafiaga P. A review of feature selection techniques in bioinformatics［J］.Bioinformatics,2007,23 (19):25072517.
［3］	Yu Zhiwen, Li Le, You Jane,et al.SC3:Triple spectral clustering based consensus clustering framework for class discovery from cancer gene expression profiles［J］.IEEE/ACM Transactions on Computational Biology and Bioinformatics,2012,9(6):17511765.
［4］	Benso A,Carlo S D,Politano G.A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics,2011,8(3):577591.
［5］	Chen Z,Li J,Wei L.A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue［J］.Artificial Intelligence in Medicine,2007,41(2):161175.
［6］	Zhai Y T,Tan M K,Tsang I W,et al.Discovering support and affiliated features from very high dimensions ［C］∥Proc of the 29th International Conference on Machine Learning,2012:1.
［7］	EinDor L,Kela I,Getz G,et al.Outcome signature genes in breast cancer:Is there a unique set? ［J］.Bioinformatics,2005,21(2):171178.
［8］	Yu L, Ding C, Loscalzo S. Stable feature selection via dense feature groups ［C］∥Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2008:803811.
［9］	Saeys Y,Abeel T,Van de P Y.Robust feature selection using ensemble feature selection techniques ［M］∥Machine Learning and Knowledge Discovery in Databases. Berlin:Springer Berlin Heidelberg,2008:313325.
［10］	Lee Z J.An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer ［J］.Artificial Intelligence in Medicine,2008,42(1):8193.
［11］	Cao Yan,Wang Qian,Zhou Chi.Research on feature selection and its ensemble method［J］.Computer Engineering & Science,2013,35 (8):168173.(in Chiense)
［12］	Lin C,Chen W,Qiu C,et al.LibD3C:Ensemble classifiers with a clustering and dynamic selection strategy［J］.Neurocomputing,2014,123:424435.
［13］	Handl J, Knowles J. Feature subset selection in unsupervised learning via multiobjective optimization ［J］.International Journal of Computational Intelligence Research,2006,2(3):217238.
［14］	Liu H W, Liu L,Zhang H J. Ensemble gene selection by grouping for microarray data classification ［J］.Journal of Biomedical Informatics,2010,43(1):8187.
［15］	Mao Yong,Zhou Xiaobo,Xia Zheng,et al.A survey for study of feature selection algorithms［J］.Pattern Recognition and Artificial Intelligence,2007,20 (2):211218.(in Chinese)
［16］	Liu H W,Liu L,Zhang H J.Feature selection using mutual information:An experimental study ［C］∥Proc of the 14th Pacific Rim International Conference on Artificial Intelligence,2012:235246.
	附中文参考文献：
［11］	曹彦,王倩,周驰.基于特征选择技术的集成方法研究［J］.计算机工程与科学,2013,35(8):168173.
［15］	毛勇,周晓波,夏铮,等.特征选择算法研究综述［J］.模式识别与人工智能,2007,20(2):211218.

[1]	刘振超, 苑迎春, 王克俭, 何晨. 融合特征权重与改进粒子群优化的特征选择算法[J]. 计算机工程与科学, 2024, 46(02): 282-291.
[2]	钟卓辉, 陈黎飞, . 基于模型的非凸聚类算法[J]. 计算机工程与科学, 2024, 46(02): 292-302.
[3]	赵瑞平, 降爱莲. 基于自编码器和局部嵌入的无监督特征选择[J]. 计算机工程与科学, 2023, 45(07): 1282-1291.
[4]	顾楚梅, 曹建军, 王保卫, 徐雨芯, . 基于蚁群参数优化的LightGBM辐射源个体识别[J]. 计算机工程与科学, 2023, 45(01): 85-94.
[5]	文武, 万玉辉, 文志云, . 基于正余弦算法的文本特征选择[J]. 计算机工程与科学, 2022, 44(08): 1467-1473.
[6]	刘云, 肖添, 王梓宇. 动态特征选择算法对恶意行为检测的优化研究[J]. 计算机工程与科学, 2022, 44(04): 665-673.
[7]	吴尚智, 徐丹丹, 王旭文, 夏宁. 基于广义重要度和runner-root算法的特征选择[J]. 计算机工程与科学, 2022, 44(04): 723-729.
[8]	徐景秀, 张青. 改进小波软阈值函数在图像去噪中的研究应用[J]. 计算机工程与科学, 2022, 44(01): 92-101.
[9]	李雨晨, 魏巍, 白伟明, 王达. 基于标签共现关系的多标签特征选择[J]. 计算机工程与科学, 2021, 43(11): 2049-2055.
[10]	文武, 万玉辉, 张许红, 文志云, . 基于改进CHI和PCA的文本特征选择[J]. 计算机工程与科学, 2021, 43(09): 1645-1652.
[11]	王涛, 全海燕. 低信噪比下联合训练生成对抗网络的语音分离[J]. 计算机工程与科学, 2021, 43(06): 1088-1094.
[12]	李向军1,2，孔珂2，魏智翔1，王科选1，肖聚鑫1. 面向Android恶意应用静态检测的特征频数差异增强算法[J]. 计算机工程与科学, 2020, 42(06): 993-1002.
[13]	孟昱煜，陈绍立，刘兴长. 面向排序学习的层次聚类特征选择算法[J]. 计算机工程与科学, 2019, 41(12): 2211-2216.
[14]	张守宾，朱习军. 集成学习算法在中医证型分类预测中的应用[J]. 计算机工程与科学, 2019, 41(02): 328-334.
[15]	纪明君，刘漫丹，才乐千. 基于半监督LDA特征子空间优化的人脸识别算法[J]. 计算机工程与科学, 2018, 40(10): 1851-1857.