Computer Science ›› 2014, Vol. 41 ›› Issue (12): 226-230.doi: 10.11896/j.issn.1002-137X.2014.12.049

Previous Articles     Next Articles

Improved RELM Based on Fish Swarm Optimization Algorithm and Cholesky Decomposition for Gene Expression Data Classification

LU Hui-juan,WEI Sha-sha,GUAN Wei and MIAO Yan-zi   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The paper proposed an improved algorithm of regular extreme learning machine(FSC-RELM) based on fish swarm optimization algorithm and Cholesky decomposition to apply in classification of gene expression data.Firstly,fish swarm optimization algorithm is used to optimize the weights of input layer and the value of objective function is defined as the reciprocal of error function.For improving the speed of the algorithm and reducing the training time,Cholesky decomposition is used on RELM output layer weights matrix.The experiments on the standard genetic data sets show that the FSC-RELM algorithm in a relatively short period of time can obtain higher classification accuracy and good performance.

Key words: Fish swarm optimization,Regularized extreme learning machine,Cholesky decomposition,Gene expression data

[1] DeRisi J,Penland L,Brown P O,et al.Use of a cDNA microa-rray to analyse gene expression patterns in human cancer[J].Nature Genetics,1996,14:457-460
[2] Zheng C H,Huang D S,Kong X Z,et al.Gene expression data classification using consensus independent component analysis[J].Genomics Proteomics and Bioinformatics,2008,6:74-78
[3] 林亚平,刘云中,周顺先,等.基于最大熵的隐马尔可夫模型文本信息抽取[J].电子学报,2005,33(2):236-240
[4] Alon U,Barkai N,Notterman D A,et al.Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[J].Proc.Natl Acad.Sci.,1999,96:6745-6750
[5] Raychaudhuri S,Stuart J M,Altman R B.Principal components analysis to summarize microarray experiments:application to sporulation time series[C]∥Pacific Symposium on Biocompu-ting.Honolulu,Hawaii,USA,2000:452-463
[6] Khan J,Bittner M,Chen Y,et al.DNA microarray technology:the anticipated impact on the study of human disease[J].Biochimica at Biophysica Acta,1999,1423:17-28
[7] Narayanan A,Tatineni S S,Gamalielsson J,et al.Reverse engineering causal networks from multiple myeloma gene expression data.http://www.dcs.ex.ac.uk/~anarayan/publications/myeloma_paper1.pdf,2002
[8] Ramaswamy S,Tamayo P,Rifkin R,et al.Multiclass Cancer Diagnostic Using Tumor Gene Expression Signatures[J].Procee-dings of the National Academy of Sciences,2001,98(26):15149-15154
[9] 陆慧娟,陆江江,王明怡,等.基于压缩感知的癌症基因表达数据分类[J].中国计量学院学报,2012,23(1):70-74
[10] Huang Guang-bin,Zhu Qin-yu,Siew chee-kheong.Extremelearning machine:Theory and applications[J].Neurocompu-ting,2006,70(1-3):489-501
[11] 高光勇,蒋国平.采用优化极限学习机的多变量混沌时间序列预测[J].物理学报,2012,61(4):1-9
[12] Huang Guang-bin,Chen L,Siew C K.Universal approximation using incremental feedforward networks with arbitrary input weights[J].Neural Networks,2006,17(4):879-892
[13] 陆慧娟,安春霖,马小平,等.基于输出不一致测度的极限学习机集成的基因表达数据分类[J].计算机学报,2013,36(2):341-348
[14] Deng Wan-yu,Chen L.Regularized extreme learning machine[C]∥Proc.IEEE Symp.Comput.Intell.Data Mining.2009:385-389
[15] Man Zhi-hong,Lee K,Wang Dian-hui,et al.Robust single-hidden layer feedforward network-based pattern classifier[J].IEEE Transactions on Neural Networks and Learning Systems,2012,23(12)
[16] Huang Guang-bin,Wang D H,Lan Y.Extreme learning ma-chines:A survey[J].Int.J.Mach.Learn.Cybern,2011,2(2):107-122
[17] Haykin S.Neural Networks:A Comprehensive Foundation[D].New Jersey:Prentice Hall,1999
[18] Man Zhi-hong,Lee K,Wang Dian-hui,et al.An optimal weight learning machine for handwritten digit image recognition[J].Signal Processing,2013,93:1624-1638
[19] Holland J H.The psychology of vocational choice:A theory of personality types and model environments[M].1965
[20] 刘金勇,郑恩辉,陆慧娟.基于聚类和微粒群优化的基因选择方法[J].数据采集与处理,2014,1(29):83-89

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!