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- 2018
基于人工蜂群和SVM的基因表达数据分类
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Abstract:
摘要： 基因表达数据存在高维、小样本、高噪声等特性,使得相应的肿瘤分类诊断面临着一定的挑战。为了实现更加精确的分类准确率,利用人工蜂群(artificial bee colony, ABC)算法对支持向量机(support vector machine, SVM)的核函数参数和惩罚因子进行优化,采用准确率作为分类模型的适应度函数,提出一种基于ABC和SVM的基因表达数据分类方法ABC-SVM。在6种公开的肿瘤基因表达数据集上进行试验,并对比分析其他的分类方法。结果表明,在筛选得到的较少信息基因基础上,ABC-SVM可获得更高的肿瘤分类准确率,对肿瘤样本类型进行更有效的分类预测。
Abstract: The characteristics of high dimension, small sample and high noise for gene expression data raised many challenges in tumor diagnosis. In order to classify tumor gene expression data more accurately, the kernel function parameters and penalty factors of SVM(support vector machine)were optimized by ABC(artificial bee colony)algorithm, in which classification accuracy was used as the fitness function. A new gene expression data classification method based on ABC algorithm and SVM, which named ABC-SVM, was proposed. Experiments were conducted on six public tumor gene expression datasets, and other classicfication methods were compared. The results showed that ABC-SVM, a method based on fewer informative genes, could obtain higher classification accuracy, and the classification of tumor samples could be more effectively predicted
| [1] | BAO Yukun, HU Zhongyi, XIONG Tao. A PSO and pattern search based memetic algorithm for SVMs parameters optimization[J]. Neurocomputing, 2014, 117(1): 98-106. |
| [2] | SECUI D C. A new modified artificial bee colony algorithm for the economic dispatch problem[J]. Energy Conversion & Management, 2015, 89(89): 43-62. |
| [3] | 李素姝,王士同,李滔. 基于LS-SVM与模糊补准则的特征选择方法[J]. 山东大学学报(工学版), 2017, 47(3): 34-42. LI Sushu, WANG Shitong, LI Tao. A feature selection method based on LS-SVM and fuzzy supplementary criterion[J]. Journal of Shandong University(Engineering Science), 2017, 47(3): 34-42. |
| [4] | GEORGE G V S, RAJ V C. Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile[J]. International Journal of Computer Science & Engineering Survey, 2011, 2(3): 16-27. |
| [5] | 梁兴建,詹志辉. 基于双模式变异策略的改进遗传算法[J]. 山东大学学报(工学版), 2014, 44(6): 1-7. LIANG Xingjian, ZHAN Zhihui. Improved genetic algorithm based on the dual-mode mutation strategy[J]. Journal of Shandong University(Engineering Science), 2014, 44(6): 1-7. |
| [6] | LIU Yihui. Cancer identification based on DNA microarray data[C] //Processdings of the International Conference on Emerging Technologies in Knowledge Discovery and Data Mining. Nanjing, China: Springer-Verlag, 2007:153-161. |
| [7] | 谢娟英,高红超. 基于统计相关性与<i>K</i>-means的区分基因子集选择算法[J]. 软件学报, 2014, 25(9): 2050-2075. XIE Juanying, GAO Hongchao. Statistical correlation and <i>K</i>-means based distinguishable gene subset selection algorithms[J]. Journal of Software, 2014, 25(9): 2050-2075. |
| [8] | HUANG Chenglung, WANG Chiehjen. A GA-based feature selection and parameters optimization for support vector machines[J]. Expert Systems with Applications, 2006, 31(2):231-240. |
| [9] | KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics & Computation, 2009, 214(1): 108-132. |
| [10] | 刘岩,李幼军,陈萌. 基于EMD和SVM的抑郁症静息态脑电信号分类研究[J]. 山东大学学报(工学版), 2017, 47(3): 21-26. LIU Yan, LI Youjun, CHEN Meng. Research on the classification of resting state EEG signal between depression patients and normal controls by EMD and SVM methods[J]. Journal of Shandong University(Engineering Science), 2017, 47(3): 21-26. |
| [11] | LI Meng, YI Liangzhong, GAO Zhisheng, et al. Support vector machine(SVM)based on membrane computing optimization and the application for C-band radio abnormal signal identification[J]. Journal of Information & Computational Science, 2014, 11(11): 3683-3693. |
| [12] | 李颖新,阮晓钢. 基于支持向量机的肿瘤分类特征基因选取[J]. 计算机研究与发展, 2005, 42(10): 1796-1801. LI Yingxin, RUAN Xiaogang. Feature selection for cancer classification based on Support Vector Machine[J]. Journal of Computer Research and Development, 2005, 42(10): 1796-1801. |
| [13] | YU Lei, LIU Huan. Efficient feature selection via analysis of relevance and redundancy[J]. Journal of Machine Learning Research, 2004, 5(12): 1205-1224. |
| [14] | CHANG Chihchung, LIN Chihjen. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology, 2011, 2(3): 1-39. |
| [15] | SATHYANARAYANA S V, AMARAPPA S. Data classification using support vector machine(SVM), a simplified approach[J]. International Journal of Electronics & Computer Science Engineering, 2014, 3(4): 435-445. |
| [16] | SUBASI A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders[J]. Computers in Biology & Medicine, 2013, 43(5): 576-586. |
| [17] | KARABOGA D, BASTURK B. On the performance of artificial bee colony(ABC)algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697. |
| [18] | ZHANG Qiantu, FANG Liqing, MA Leilei, et al. Research on parameters optimization of SVM based on improved fruit fly optimization algorithm[J]. International Journal of Computer Theory and Engineering, 2016, 8(6): 500-505. |
| [19] | KIRAN M S, BABALIK A. Improved artificial bee colony algorithm for continuous optimization problems[J]. Journal of Computer & Communications, 2014, 02(4): 108-116. |
| [20] | TSAI H C. Integrating the artificial bee colony and bees algorithm to face constrained optimization problems[J]. Information Sciences, 2014, 258(3): 80-93. |
| [21] | ZHANG Shanwen, HUANG Deshuang, WANG Shulin. A method of tumor classification based on wavelet packet transforms and neighborhood rough set[J]. Computers in Biology & Medicine, 2010, 40(4): 430-437. |
| [22] | QUACKENBUSH J. Microarray analysis and tumor classification[J]. New England Journal of Medicine, 2006, 354(23): 2463-2472. |
| [23] | 叶明全,高凌云,伍长荣,等. 基于对称不确定性和SVM递归特征消除的信息基因选择方法[J]. 模式识别与人工智能, 2017, 30(5): 429-438. YE Mingquan, GAO Lingyun, WU Changrong, et al. Informative gene selection method based on symmetric uncertainty and SVM recursive feature elimination[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(5): 429-438. |
| [24] | 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(5439): 531-537. |
| [25] | 林俊,许露,刘龙. 基于SVM-RFE-BPSO算法的特征选择方法[J]. 小型微型计算机系统, 2015, 36(8):1865-1868. LIN Jun, XU Lu, LIU Long. Feature selection based on SVM-RFE and particle swarm optimization[J]. Journal of Chinese Computer Systems, 2015, 36(8):1865-1868. |
| [26] | 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. LU Huijuan, AN Chunlin, MA Xiaoping, et al. Disagreement measure based ensemble of extreme learning machine for gene expression data classification[J]. Chinese Journal of Computers, 2013, 36(2): 341-348. |
| [27] | KAR S, SHARMA K D, MAITRA M. Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique[J]. Expert Systems with Applications, 2015, 42(1): 612-627. |
| [28] | 谢娟英,谢维信. 基于特征子集区分度与支持向量机的特征选择算法[J]. 计算机学报, 2014, 37(8): 1704-1718. XIE Juanying, XIE Weixin. Several feature selection algorithms based on the discernibility of a feature subset and support vector machines[J]. Chinese Journal of Computers, 2014, 37(8): 1704-1718. |
| [29] | KHALILI M, MAJD H A, KHODAKARIM S, et al. Prediction of the thromboembolic syndrome: an application of artificial neural networks in gene expression data analysis[J]. Journal of Paramedical Sciences, 2016, 7(2): 15-22. |
| [30] | CORTES C, VAPNIK V. Support vector networks[J]. Machine Learning, 1995, 20(3):273-297. |
| [31] | ZHANG Xiaoli, CHEN Xuefeng, HE Zhengjia, et al. An ACO-based algorithm for parameter optimization of support vector machines[J]. Expert Systems With Applications, 2010, 37(9): 6618-6628. |
| [32] | KARABOGA D, GORKKEMLI B, OZTURK C, et al. A comprehensive survey: artificial bee colony(ABC)algorithm and applications[J]. Artificial Intelligence Review, 2014, 42(1): 21-57. |
| [33] | 秦全德,程适,李丽,等.人工蜂群算法研究综述[J]. 智能系统学报,2014,9(2):127-135. QIN Quande, CHENG Shi, LI Li, et al. Artificial bee colony algorithm: a survey[J]. CAAI Transactions on Intelligent Systems, 2014, 9(2): 127-135. |
