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改进的离散PSO和SVM的特征基因选择算法

DOI: 10.3969/j.issn.1006-7043.2009.12.010

Keywords: 离散粒子群 特征基因 支持向量机 局部最优 discrete particle swarm optimization feature gene support vector machine local optimum

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Abstract:

针对现有的基于粒子群的特征基因选择算法易于陷入局部最优的问题,提出了一种改进的离散粒子群和支持向量机的特征基因选择算法IDPSO-SVM.该算法首先预选一些与分类强相关的基因组成特征基因备选集合,然后基于此集合采用PSO进行寻优搜索,并应用SVM对选出的特征子集的分类能力进行评估,最后得出最优特征子集.该算法加入了一种可以有效克服粒子群在寻优过程中陷入局部最优的机制,因而可以不断探测到新的最优解.该算法在结肠癌与前列腺癌数据集上的分类精度分别达到了96.8%与99.0%,从而证明了其有效性与可行性.

References

[1]  2. GOLUB T R,SLONIM D K,TAMAYO P,et al.Molecular classification of cancer:class discovery and class prediction by gene expression monitoring[J].Science,1999,286:531-537.
[2]  3. GUYPN I,WESTON J,BARNHILL S,et al.Gene selection for cancer classification using support vector machines[J].Machine Learning,2002,46(1):389-422.
[3]  4. INZA I,LARRANAGA P,BLANCO R.Filter versus wrapper gene selection approaches in DNA microarray domains[J].Artificial Intelligence in Medicine,2004,31(2):91-104. 5. PENG S H,XU Q H,FENG X,et al.Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines[J].FEBS Letters,2003,555(2):358-362.
[4]  13. WANG Y H,MAKEDON F S,FORD J C.HykGene:a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data[J].Bioinformatics,2005,21(8):1530-1537.
[5]  1. 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 array[J].Proceedings of the National Academy of Sciences,1999,96(12):6745-6750.
[6]  6. SHEN Q,SHI W M,KONG W,et al.A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification[J].Talanta,2007,71(4):1679-1683.
[7]  7. KENNEDY J,EBERHART R C.Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks.Piscataway NJ:IEEE Press,1995,4:1942-1948.
[8]  8. KENNEDY J,EBERHART R C.A discrete binary version of the particle swarm algorithm[C]// IEEE Conference on Systems,Man,and Cybernetics.Piscataway NJ:IEEE Press,1997,5:4104-4109.
[9]  9. VAPNIK V.Statistical learning theory[M].New York:Wiley-Interscience Publication,1998:35-70.
[10]  10. SINGH D,FEBBO P,ROSS K,et a1.Gene expression correlates of clinical prostate cancer behavior[J].Cancer Cell,2002,1(2):203-209.
[11]  11. GUNN S R.Support vector machines for classification and regression[EB/OL].[2008-07-.http://users.ecs.soton.ac.uk/srg/publi catio-ns/pdf/SVM.pdf.
[12]  12. PENG Y H.A novel ensemble machine learning for robust microarray data classification[J].Computers in Biology and Medicine,2006,36(6):553-573.
[13]  14. LIU B,CUI Q H,JIANG T Z,et al.A combinational feature selection and ensemble neural network method for classification of gene expression data[J].BMC Bioinformatics,2004,5:136.
[14]  15. 满春涛,孙明辉,张礼勇.粒子群优化算法在多峰函数寻优上的应用[J].哈尔滨理工大学学报,2007,12(2):11-13,18.MAN Chuntao,SUN Minghui,ZHANG Liyong.Application of particle swarms optimization algorithm on multi-modality function optimization[J].Journal of Harbin University of Science and Technology,2007,12(2):11-13,18.

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