面向高维小样本数据的分类特征选择算法研究
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摘要
高维小样本数据在实际应用中相当普遍,例如自然语言处理中的文本数据、计算机视觉中的图像数据、生物信息学中的基因表达谱数据等,给现有的挖掘和学习算法带来了巨大的挑战。随着数据维度的急剧增加,会产生大量的无关以及冗余信息,这些信息可能极大降低机器学习算法的性能,增加计算复杂度,造成“维数灾难”以及“过拟合”问题。特征选择是解决高维小样本问题的一种有效手段,它可以去除大量不相关和冗余的特征,寻找与分类任务强相关的特征子集,从而减少算法运行时间,提高算法精度。因此,开展高维小样本数据环境下的特征选择方法研究具有重要的研究与应用价值。
本文选择真实的基因表达谱数据作为具体实验对象,将特征选择算法应用于疾病分类问题中,并把分类结果的好坏作为我们特征选择算法的评价指标之—围绕高维小样本的特征选择问题,本文开展了一系列的研究工作,主要研究成果包括以下几个方面：
(1)针对高维小样本数据会导致“维数灾难”的问题,我们提出一种嵌入特征选择方法K-split Lasso来降维,提高分类模型的精度,解决计算复杂度高的问题。K-split Lasso是基于经典的Lasso方法提出的,其基本思想是将数据集平均划分为K份,分别使用Lasso方法对每份进行特征选择,而后将选择出来的每份特征子集合并,重新进行特征选择,从而得到最终的特征子集。实验结果表明K-split Lasso算法提高了模型的分类精度,在一定程度上解决了“维数灾难”问题。
(2)针对高维小样本数据会导致“过拟合”问题,我们结合过滤方法和嵌入方法的优点,并在此基础上提出一种新的混合特征选择方法GSIL,目的是从高维数据中选出具有强类别区分能力的特征子集,解决“过拟合”问题。GSIL方法分为两层,第一层采用信噪比指标衡量特征的重要性,以过滤无关特征；第二层采用改进的Lasso方法(Iterative Lasso)进行冗余特征的剔除。实验结果表明,GSIL算法能够有效提高分类模型的精度,减少了冗余特征,解决了“过拟合”问题,通过与已有的一些特征选择方法进行了分析比较,也验证了GSIL方法的可行性和有效性。
(3)针对高维小样本数据会造成特征选择算法的不稳定性,我们利用集成学习方法来提高分类模型的预测能力以及特征选择的稳定性。考虑到目前已经提出的大多特征选择方法仅根据区分能力选择单个特征子集,虽然这些子集可以在一定程度上提高学习模型的性能,但是由于单个子集包含的信息量有限,会导致特征选择算法的不稳定性。因此,本文提出一种基于相关性的集成特征选择算法ECGS-RG,生成多个有效的特征子集来弥补单个子集信息量的不足,提高特征选择的稳定性,该方法主要利用信息度量标准和Approximate Markov blanket技术作为评估特征与已选特征子集之间相关性的指标。实验结果表明ECGS-RG集成特征选择算法的性能以及稳定性在多数情况下均优于只选择单个特征子集的方法。
The data sets with high dimensions and small samples are very common in practical applications, such as text data in natural language processing, image data in computer vision, gene expression profiles in bioinformatics, etc. It is hence a challenge for the existing learning algorithms. With the rapid increasing of the data dimension, there are lots of irrelevant and redundant information in these data, which may greatly deteriorate the performance of the machine learning algorithms, increase the computational complexity and meanwhile lead to the problems of "Curse of Dimensionality" and "Over-Fitting". However, feature selection is an efficient way to solve the problem of high dimensions and small samples. This is because feature selection can remove a large number of irrelevant and redundant features, and find a compact feature subset with a high classification accuracy. Thus, it is very significant in both fileds of research and application.
In this dissertation, we select gene expression profiles as the experimental data. Feature selection algorithms are applied in the handling of disease classification problem, and the classification accuracy is one of the evaluation indicators of these algorithms. Our work focuses on feature selection for high-dimensional and small-sampling data and our main contributions are as follows:
1) Since high-dimensional and small-sampling data could lead to "Curse of Dimensionality", we propose an embedded feature selection algorithm called K-split Lasso. It aims to reduce the data dimensionality for improving the classification accuracy and solve the problem of high computational complexity. First, divide the feature sets into K parts in K-split Lasso, and then select the features from each feature subset using Lasso, finally merge the selected genes into one feature subset. Our experimental results demonstrate that K-split Lasso can improve the prediction accuracy of the classification models, and to some extent, it can solve the problem of "Curse of Dimensionality".
2) Since the high-dimensional and small-sampling data could lead to "Over-Fitting", we present a new hybrid feature selection algorithm called GSIL. It aims to select a small set of important features more relevant to the classification task. In our approach, we first apply the feature ranking algorithm Signal Noise Ratio to filter irrelevant features, and then apply Iterative Lasso to eliminate the redundant features. Empirical studies demonstrate that our approach can reduce data redundancy for improving the classification accuracy, and solve the problem of "Over-Fitting". Moreover, the effectiveness of GSIL is verified by comparing with several known feature selection methods.
3) Since high-dimensional and small-sampling data could lead to the instability of feature selection algorithm, we use an ensemble learning technique to improve the prediction accuracy of classification models and the stability of feature selection. Currently, most existing feature selection methods only choose an individual small feature subset according to the discriminative power. Although these methods could improve the performance of learning models, the selected subset is prone to result in the instability for its relative limitted amount of information. Thus, we present an ensemble correlation-based feature selection approach ECGS-RG It aims to generate various effective feature subsets for making up the insufficiency of an individual feature subset. ECGS-RG applies information metrics and approximate Markov blanket technique to evaluate the correlation between the candidate feature and the selected subset. Experimental results show that the classification performance and the stability of ECGS-RG algorithm is superior to those only select an individual feature subset in most cases.
本文选择真实的基因表达谱数据作为具体实验对象,将特征选择算法应用于疾病分类问题中,并把分类结果的好坏作为我们特征选择算法的评价指标之—围绕高维小样本的特征选择问题,本文开展了一系列的研究工作,主要研究成果包括以下几个方面：
(1)针对高维小样本数据会导致“维数灾难”的问题,我们提出一种嵌入特征选择方法K-split Lasso来降维,提高分类模型的精度,解决计算复杂度高的问题。K-split Lasso是基于经典的Lasso方法提出的,其基本思想是将数据集平均划分为K份,分别使用Lasso方法对每份进行特征选择,而后将选择出来的每份特征子集合并,重新进行特征选择,从而得到最终的特征子集。实验结果表明K-split Lasso算法提高了模型的分类精度,在一定程度上解决了“维数灾难”问题。
(2)针对高维小样本数据会导致“过拟合”问题,我们结合过滤方法和嵌入方法的优点,并在此基础上提出一种新的混合特征选择方法GSIL,目的是从高维数据中选出具有强类别区分能力的特征子集,解决“过拟合”问题。GSIL方法分为两层,第一层采用信噪比指标衡量特征的重要性,以过滤无关特征；第二层采用改进的Lasso方法(Iterative Lasso)进行冗余特征的剔除。实验结果表明,GSIL算法能够有效提高分类模型的精度,减少了冗余特征,解决了“过拟合”问题,通过与已有的一些特征选择方法进行了分析比较,也验证了GSIL方法的可行性和有效性。
(3)针对高维小样本数据会造成特征选择算法的不稳定性,我们利用集成学习方法来提高分类模型的预测能力以及特征选择的稳定性。考虑到目前已经提出的大多特征选择方法仅根据区分能力选择单个特征子集,虽然这些子集可以在一定程度上提高学习模型的性能,但是由于单个子集包含的信息量有限,会导致特征选择算法的不稳定性。因此,本文提出一种基于相关性的集成特征选择算法ECGS-RG,生成多个有效的特征子集来弥补单个子集信息量的不足,提高特征选择的稳定性,该方法主要利用信息度量标准和Approximate Markov blanket技术作为评估特征与已选特征子集之间相关性的指标。实验结果表明ECGS-RG集成特征选择算法的性能以及稳定性在多数情况下均优于只选择单个特征子集的方法。
The data sets with high dimensions and small samples are very common in practical applications, such as text data in natural language processing, image data in computer vision, gene expression profiles in bioinformatics, etc. It is hence a challenge for the existing learning algorithms. With the rapid increasing of the data dimension, there are lots of irrelevant and redundant information in these data, which may greatly deteriorate the performance of the machine learning algorithms, increase the computational complexity and meanwhile lead to the problems of "Curse of Dimensionality" and "Over-Fitting". However, feature selection is an efficient way to solve the problem of high dimensions and small samples. This is because feature selection can remove a large number of irrelevant and redundant features, and find a compact feature subset with a high classification accuracy. Thus, it is very significant in both fileds of research and application.
In this dissertation, we select gene expression profiles as the experimental data. Feature selection algorithms are applied in the handling of disease classification problem, and the classification accuracy is one of the evaluation indicators of these algorithms. Our work focuses on feature selection for high-dimensional and small-sampling data and our main contributions are as follows:
1) Since high-dimensional and small-sampling data could lead to "Curse of Dimensionality", we propose an embedded feature selection algorithm called K-split Lasso. It aims to reduce the data dimensionality for improving the classification accuracy and solve the problem of high computational complexity. First, divide the feature sets into K parts in K-split Lasso, and then select the features from each feature subset using Lasso, finally merge the selected genes into one feature subset. Our experimental results demonstrate that K-split Lasso can improve the prediction accuracy of the classification models, and to some extent, it can solve the problem of "Curse of Dimensionality".
2) Since the high-dimensional and small-sampling data could lead to "Over-Fitting", we present a new hybrid feature selection algorithm called GSIL. It aims to select a small set of important features more relevant to the classification task. In our approach, we first apply the feature ranking algorithm Signal Noise Ratio to filter irrelevant features, and then apply Iterative Lasso to eliminate the redundant features. Empirical studies demonstrate that our approach can reduce data redundancy for improving the classification accuracy, and solve the problem of "Over-Fitting". Moreover, the effectiveness of GSIL is verified by comparing with several known feature selection methods.
3) Since high-dimensional and small-sampling data could lead to the instability of feature selection algorithm, we use an ensemble learning technique to improve the prediction accuracy of classification models and the stability of feature selection. Currently, most existing feature selection methods only choose an individual small feature subset according to the discriminative power. Although these methods could improve the performance of learning models, the selected subset is prone to result in the instability for its relative limitted amount of information. Thus, we present an ensemble correlation-based feature selection approach ECGS-RG It aims to generate various effective feature subsets for making up the insufficiency of an individual feature subset. ECGS-RG applies information metrics and approximate Markov blanket technique to evaluate the correlation between the candidate feature and the selected subset. Experimental results show that the classification performance and the stability of ECGS-RG algorithm is superior to those only select an individual feature subset in most cases.
引文
[1]Chen M.S., Han J.W., Yu P.S. Data mining:an overview from a database perspective [J]. IEEE Transactions on Knowledge and Data Engineering,1996,8(6):866-883.
[2]Fodor I.K. A survey of dimension reduction techniques. Lawrence Livermore National Laboratory, U.S. Department of Energy,2002.
[3]Wang S.L., Li X.L., Fang J.W. Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification [J]. BMC Bioinformatics,2012,13:178.
[4]Handl J., Knowles J. Feature Subset Selection in Unsupervised Learning via Multiobjective optimization [J]. International Journal of Computational Intelligence Research,2006,2(3): 217-238.
[5]Han J.W., Kamber M., Pei J. Data mining:concepts and techniques [M]. Morgan kaufmann, 2006.
[6]Kotsiantis S.B. Supervised machine learning:a review of classification technique [J]. Informatica,2007,31:249-268.
[7]Larose D.T. Discovering knowledge in data:an introduction to data mining [M]. Wiley, com, 2005.
[8]Ngai E.W.T, Xiu L., Chau D.C.K. Application of data mining techniques in customer relationship management:A literature review and classification [J]. Expert Systems with Applications,2009,36(2):2592-2602.
[9]Witten I.H., Frank E. Data Mining:Practical machine learning tools and techniques [M]. Morgan Kaufmann,2005.
[10]Quinlan J.R. C4.5:Programs for Machine Learning [M]. CA:Morgan Kaufmann,1993.
[11]Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition:A review [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[12]徐燕,李锦涛,王斌等.基于区分类别能力的高性能特征选择方法[J].软件学报,2008,19(1)：82-89.
[13]Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy [J]. The Journal of Machine Learning Research,2004,5:1205-1224.
[14]Koller D., Sahami M. Toward optimal feature selection [C]. In Proceedings of the 13th International Conference on Machine Learning,1996, pp.284-292.
[15]Zhao Z., Wang L., Liu H., Ye J.P. On similarity preserving feature selection [J]. IEEE Transactions on Knowledge and Data Engineering,2013,25(3):619-632.
[16]Hall M.A. Correlation-based feature selection for discrete and numeric class machine learning [C]. In Proceedings of the 7th International Conference on Machine Learning,2000, pp. 359-366.
[17]Yu L., Liu H. Redundancy based feature selection for microarray data [C]. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, pp.737-742.
[18]刘峤.基于最短描述长度的高维特征选择方法研究[D].成都：电子科技大学信息安全专业,2010.
[19]陈友,程学旗,李洋等.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7)：1639-1651.
[20]Narendra P.M., Fukunaga K. A branch and bound algorithm for feature subset selection [J]. IEEE Transactions on Computers,1977,100(9):917-922.
[21]Davies S., Russell S. NP-completeness of searches for smallest possible feature sets [C].In Proceedings of the AAAI Fall'94 Symposium on Relevance, New Orleans,1994, pp.37-39.
[22]Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(4):491-502.
[23]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.
[24]Dash M., Liu H. Feature selection for classification [J]. Intelligent data analysis,1997,1(3): 131-156.
[25]Molina L.C., Belanche L., Nebot A. Feature selection algorithms:A survey and experimental evaluation [C]. Li Proceedings of the 2nd IEEE International Conference on Data Mining, 2002, pp.306-313.
[26]Yu L., Liu H. Feature selection for high-dimensional data:A fast correlation-based filter solution [C].In Proceedings of the 20th International Conference on Machine Learning,2003, pp.856-863.
[27]Peng H.C., Long F., Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.
[28]Dash M., Liu H. Consistency-based search in feature selection [J]. Artificial intelligence,2003, 151(1):155-176.
[29]Almuallim H., Dietterich T.G. Learning boolean concepts in the presence of many irrelevant features [J]. Artificial Intelligence,1994,69(1):279-305.
[30]Dash M. Feature selection via set cover [C].In Proceedings of IEEE Knowledge and Data Engineering Exchange Workshop,1997, pp.165-171.
[31]Li L.P., Weinberg C.R., Darden T.A., Pedersen L.G. Gene selection for sample classification based on gene expression data:study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics,2001,17(12):1131-1142.
[32]Chen X.W. Margin-based wrapper methods for gene identification using microarray. Neurocomputing,2006,69(16-18):2236-2243.
[33]Akaike H. Information theory and an extension of the maximum likelihood principle [C]. In Second International Symposium on Information Theory. Akademinai Kiado,1973, pp. 267-281.
[34]Rissanen J. Modeling by shortest data description [J]. Automatica,1978,14(5):465-471.
[35]Blum A.L., Langley P. Selection of relevant features and examples in machine learning [J]. Artificial intelligence,1997,97(1):245-271.
[36]Kohavi R., John G.H. Wrappers for feature subset selection [J]. Artificial intelligence,1997, 97(1):273-324.
[37]Guyon I., Elisseeff A. An introduction to variable and feature selection [J]. The Journal of Machine Learning Research,2003,3:1157-1182.
[38]Smialowski P.. Frishman D., Kramer S. Pitfalls of supervised feature selection [J]. Bioinformatics,2010,26(3):440-443.
[39]Forman G. An extensive empirical study of feature selection metrics for text classification [J]. The Journal of machine learning research,2003,3:1289-1305.
[40]Mitra P., Murthy C.A., Pal S.K. Unsupervised feature selection using feature similarity [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(3):301-312.
[41]Dy J.G., Brodley C.E. Feature selection for unsupervised learning [J]. The Journal of Machine Learning Research,2004,5:845-889.
[42]Zhao J.D., Lu K., He X.F. Locality sensitive semi-supervised feature selection [J]. Neurocomputing,2008,71(10):1842-1849.
[43]Zhao Z., Liu H. Semi-supervised Feature Selection via Spectral Analysis [C].In Proceedings of the 7th SIAM International Conference on Data Mining,2007, pp.641-646.
[44]Song Y.Q., Nie F.P., Zhang C.S., Xiang S.M. A unified framework for semi-supervised dimensionality reduction [J]. Pattern Recognition,2008,41(9):2789-2799.
[45]Wang Y.H., Makedon F.S., Ford J.C., Pearlman J. HykGene:a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data [J]. Bioinformatics,2005,21(8):1530-1537.
[46]Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring [J]. Science,1999,286(5439):531-537.
[47]Cheng Q., Zhou H.B., Cheng J. The Fisher-Markov selector:fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(6): 1217-1233.
[48]Song L., Smola A., Gretton A., Bedo J., Borgwardt K. Feature selection via dependence maximization [J]. The Journal of Machine Learning Research,2012,98888(1):1393-1434.
[49]Hsu W.H. Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning [J]. Information Sciences,2004,163(1): 103-122.
[50]Chuang L.Y., Yang C.H., Li J.C., Yang C.H. A hybrid BPSO-CGA approach for gene selection and classification of microarray data [J]. Journal of Computational Biology,2012,19(1): 68-82.
[51]Rodrigues D., Pereira L.A.M., Nakamura R.Y.M., Costa K.A.P., Yang X.S., Souza A.N., Papa J.P. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest [J]. Expert Systems with Applications,2014,41(5):2250-2258.
[52]Papa J.P., Falcao A.X., De Albuquerque V.H.C., Tavares J.M.R.S. Efficient supervised optimum-path forest classification for large datasets [J]. Pattern Recognition,2012,45(1): 512-520.
[53]Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines [J]. Machine learning,2002,46(1-3):389-422.
[54]Tan M., Wang L., Tsang I.W. Learning sparse svm for feature selection on very high dimensional datasets [C]. In Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010, pp.1047-1054.
[55]Ma S.G., Song X., Huang J. Supervised group Lasso with applications to microarray data analysis [J]. BMC Bioinformatics,2007,8:60.
[56]Maldonado S., Weber R., Basak J. Simultaneous feature selection and classification using kernel-penalized support vector machines [J]. Information Sciences,2011,181(1):115-128.
[57]Yang P.Y., Zhou B.B., Zhang Z.L., Zomaya A.Y. A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data [J]. BMC bioinformatics,2010, 11(Suppl 1):S5.
[58]Sebban M., Nock R. A hybrid filter/wrapper approach of feature selection using information theory [J]. Pattern Recognition,2002,35(4):835-846.
[59]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.
[60]王树林,王戟,陈火旺,李树涛,张波云.肿瘤信息基因启发式宽度优先搜索算法研究[J].计算机学报,2008,31(4)：636-649.
[61]Akadi A.E., Amine A, Ouardighi A.E., Aboutajdine D. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper [J]. Knowledge and Information Systems,2011,26(3): 487-500.
[62]Cadenas J.M., Garrido M.C., MartiNez R. Feature subset selection Filter-Wrapper based on low quality data [J]. Expert Systems with Applications,2013,40(16):6241-6252.
[63]Loscalzo S., Yu L., Ding C. Consensus group stable feature selection [C]. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp.567-576.
[64]Kalousis A., Prados J., Hilario M. Stability of feature selection algorithms:a study on high-dimensional spaces [J]. Knowledge and information systems,2007,12(1):95-116.
[65]Han Y., Yu L. A variance reduction framework for stable feature selection [J]. Statistical Analysis and Data Mining,2012,5(5):428-445.
[66]Saeys Y., Abeel T., Van de P.Y. Robust feature selection using ensemble feature selection techniques [M]. Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg,2008, pp.313-325.
[67]Davis C.A., Gerick F., Hintermair V., Friedel C.C., Fundel K., Kuffner R., Zimmer R. Reliable gene signatures for microarray classification:assessment of stability and performance [J]. Bioinformatics,2006,22(19):2356-2363.
[68]Ein-Dor L., Kela I., Getz G., Givol D., Domany E. Outcome signature genes in breast cancer: is there a unique set? [J]. Bioinformatics,2005,21(2):171-178.
[69]Awada W., Khoshgoftaar T.M., Dittman D., Wald R., Napolitano A. A review of the stability of feature selection techniques for bioinformatics data [C]. In Proceedings of the 13th International Conference on Information Reuse and Integration,2012, pp.356-363.
[70]Yu L., Han Y, Berens M.E. Stable gene selection from microarray data via sample weighting [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics,2012,9(1): 262-272.
[71]Hassan M.R., Hossain M.M., Bailey J., Macintyre G, Ho J.W., Ramamohanarao K. A voting approach to identify a small number of highly predictive genes using multiple classifiers [J]. BMC bioinformatics,2009,10(Suppl 1):S19.
[72]Li X., Rao S.Q., Wang Y.D., Gong B.S. Gene mining:a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling [J]. Nucleic Acids Research,2004,32(9):2685-2694.
[73]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.
[74]Brock G.N., Shaffer J.R., Blakesley R.E., Lotz M.J., Tseng G.C. Which missing value imputation method to use in expression profiles:a comparative study and two selection schemes [J]. BMC Bioinformatics,2008,9:12.
[75]李颖新,李建更,阮晓钢.肿瘤基因表达谱分类特征基因选取问题及分析方法研究[J].计算机学报,2006,29(2)：324-330.
[76]Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics [J]. Bioinformatics,2007,23(19):2507-2517.
[77]Ramon D.U., Sara A.A. Gene selection and classification of microarray data using random forest [J]. BMC Bioinformatics,2006,7:3.
[78]李瑶.基因芯片数据分析与处理[M].北京：化学工业出版社,2006.
[79]于化龙.基于DNA微阵列数据的癌症分类技术研究[D].哈尔滨：哈尔滨工程大学计算机应用技术专业,2010.
[80]Tibshirani R. Regression shrinkage and selection via the Lasso [J]. Journal of the Royal Statistical Society Series B-Methodological,1996,58(1):267-288.
[81]Efron B., Hastie T., Johnstone I., Tibshirani R. Least Angle Regression [J]. Journal of the Institute of Mathematical Statistics,2004,32(2):407-499:
[82]施万锋,胡学钢,俞奎.一种面向高维数据的均分式Lasso特征选择方法[J].计算机工程与应用,2012,48(1)：157-161.
[83]Singh D., Febbo P.G, Ross K., Jackson D.G., Manola J., Ladd C., et al. Gene expression correlates of clinical prostate cancer behavior [J]. Cancer Cell,2002,1(2):203-209.
[84]Zhao Y.D., Simon R. BRB ArrayTools data archive for human cancer gene expression:a unique and efficient data sharing resource [J]. Cancer Informatics,2008,6:9-15.
[85]Brown M.P.S., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Furey T.S., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines [J]. Proceedings of the National Academy of Sciences of the United States of America.2000,97(1):262-267.
[86]George G.V.S., Raj V.C. Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profiles [J]. International Journal of Computer Science and Engineering Survey,2011,2(3):16-27.
[87]Huang D.S., Ip H.H.S., Law K.C.K., Chi Z. Zeroing polynomials using modified constrained neural network approach [J]. IEEE Transactions on Neural Networks,2005,16(3):721-732.
[88]Huang D.S., Zheng C.H. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data [J]. Bioinformatics,2006,22(15): 1855-1862.
[89]Wang S.L., Zhu Y.H., Jia W., Huang D.S. Robust classification method of tumor subtype by using correlation filters [J]. Journal of IEEE/ACM Transactions on Computational Biology and Bioinformatics,2012,9(2):580-591.
[90]Robnik-Sikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RreliefF [J]. Machine learning,2003,53(1-2):23-69.
[91]Hanczar B., Courtine M., Benis A., Hennegar C, Clement K., Zucker J.D. Improving classification of microarray data using prototype-based feature selection [J]. ACM SIGKDD Explorations Newsletter,2003,5(2):23-30.
[92]Tan F., Fu X.Z., Wang H., Zhang Y.Q., Bourgeois A. A hybrid feature selection approach for microarray gene expression data [C]. In Proceedings of the 6th International Conference on Computational Science,2006, pp.678-685.
[93]Zheng S.F., Liu W.X. An experimental comparison of gene selction by Lasso and Dantzig selector for cancer classification [J]. Computers in Biology and Medicine,2011,41(11): 1033-1040.
[94]Liu H., Motoda H., Setiono R., Zhao Z. Feature selection:an ever evolving frontier in data mining [C]. In Proceedings of the 4th Workshop on Feature Selection in Data Mining,2010, pp.4-13.
[95]Alon U., Barkai N., Notterman D.A., Gish S., Ybarra S., Mack D., et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [C]. In Proceedings of the National Academy of Sciences of the United States of America,1999,96(12):6745-6750.
[96]Shipp M.A., Ross K.N., Tamayo P., Weng A.P., Kutok J.L., Aguiar R.C.T., et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning [J]. Nature Medicine,2002,8(1):68-74.
[97]Gordon G.J., Jensen R.V., Hsiao L.L., Gullans S.R., Blumenstock J.E., Ramaswamy S., et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma [J]. Cancer Research,2002,62:4963.
[98]Frank E., Hall M., Trigg L., Holmes G., Witten I.H. Data mining in bioinformatics using Weka [J]. Bioinformatics,2004,20(15):2479-2481.
[99]Kulkarni A., Kumar N., Ravi V., Murthy U.S. Colon cancer prediction with genetics profiles using evolutionary techniques [J]. Expert Systems with Applications,2011,38(3):2752-2757'.
[100]Shen Q., Shi W.M., Kong W., Ye B.X. A combination of modified particleswarm optimization algorithm and support vector machine for gene selection and tumor classification [J]. Talanta, 2007,71(4):1679-1683.
[101]Abeel T., Helleputte T., Van de Peer Y., Dupont P., Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods [J]. Bioinformatics,2010,26(3): 392-398.
[102]Hilario M., Kalousis A. Approaches to dimensionality reduction in proteomic biomarker studies [J]. Briefings in Bioinformatics,2008,9(2):102-118.
[103]Nam D., Kim S.Y. Gene-set approach for expression pattern analysis [J]. Briefings in bioinformatics,2008,9(3):189-197.
[104]Zhou X., Tuck D.P. MSVM-RFE:extensions of SVM-RFE for multiclass gene selection on DNA microarray data.[J]. Bioinformatics,2007,23(9):1106-1114.
[105]Wang L., Zhu J., Zou H. Hybrid huberized support vector machines for microarray classification and gene selection [J]. Bioinformatics,2008,24(3):412-419.
[106]Kursa M.B. Robustness of Random Forest-based gene selection methods [J]. BMC bioinformatics,2014,15(1):8.
[107]Zhai Y.T., Tan M.K., Tsang I.W., Ong Y.S. Discovering support and affiliated features from very high dimensions [C]. In Proceedings of the 29th International Conference on Machine Learning,2012.
[108]Yu L., Ding C, Loscalzo S. Stable feature selection via dense feature groups [C].In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2008, pp.803-811.
[109]Hong J.H., Cho S.B. The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming [J]. Artificial Intelligence in Medicine,2006,36(1): 43-58.
[110]Cho S.B., Won H.H. Cancer classification using ensemble of neural networks with multiple significant gene subsets [J]. Applied Intelligence,2007,26(3):243-250.
[111]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.
[112]Dietterich T.G. Ensemble methods in machine learning [M]. Multiple classifier systems. Springer Berlin Heidelberg,2000:1-15.
[113]Breiman L. Bagging predictors [J]. Machine learning,1996,24(2):123-140.
[114]Schapire R.E. The strength of weak learnability [J]. Machine learning,1990,5(2):197-227.
[115]王清.集成学习中若干关键问题的研究[D].上海：复旦大学计算机软件与理论专业,2011.
[116]He Z.Y., Yu W.C. Stable feature selection for biomarker discovery [J]. Computational biology and chemistry,2010,34(4):215-225.
[117]Moon H., Ahn H., Kodell R.L., Baek S., Lin C.J., Chen J.J. Ensemble methods for classification of patients for personalized medicine with high-dimensional data [J]. Artificial intelligence in medicine,2007,41(3):197-207.
[118]Dutkowski J., Gambin A. On consensus biomarker selection [J]. BMC bioinformatics,2007, 8(Suppl 5):S5.
[119]Duda R.O., Hart P.E., Stork D.G Pattern classification [M]. John Wiley & Sons, Inc,1995.
[120]Liu H.W., Liu L., Zhang H.J. Feature selection using mutual information:an experimental study [J]. In Proceedings of the 10th Pacific Rim International Conference on Attificial Intelligence,2008, pp.235-246.
[2]Fodor I.K. A survey of dimension reduction techniques. Lawrence Livermore National Laboratory, U.S. Department of Energy,2002.
[3]Wang S.L., Li X.L., Fang J.W. Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification [J]. BMC Bioinformatics,2012,13:178.
[4]Handl J., Knowles J. Feature Subset Selection in Unsupervised Learning via Multiobjective optimization [J]. International Journal of Computational Intelligence Research,2006,2(3): 217-238.
[5]Han J.W., Kamber M., Pei J. Data mining:concepts and techniques [M]. Morgan kaufmann, 2006.
[6]Kotsiantis S.B. Supervised machine learning:a review of classification technique [J]. Informatica,2007,31:249-268.
[7]Larose D.T. Discovering knowledge in data:an introduction to data mining [M]. Wiley, com, 2005.
[8]Ngai E.W.T, Xiu L., Chau D.C.K. Application of data mining techniques in customer relationship management:A literature review and classification [J]. Expert Systems with Applications,2009,36(2):2592-2602.
[9]Witten I.H., Frank E. Data Mining:Practical machine learning tools and techniques [M]. Morgan Kaufmann,2005.
[10]Quinlan J.R. C4.5:Programs for Machine Learning [M]. CA:Morgan Kaufmann,1993.
[11]Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition:A review [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[12]徐燕,李锦涛,王斌等.基于区分类别能力的高性能特征选择方法[J].软件学报,2008,19(1)：82-89.
[13]Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy [J]. The Journal of Machine Learning Research,2004,5:1205-1224.
[14]Koller D., Sahami M. Toward optimal feature selection [C]. In Proceedings of the 13th International Conference on Machine Learning,1996, pp.284-292.
[15]Zhao Z., Wang L., Liu H., Ye J.P. On similarity preserving feature selection [J]. IEEE Transactions on Knowledge and Data Engineering,2013,25(3):619-632.
[16]Hall M.A. Correlation-based feature selection for discrete and numeric class machine learning [C]. In Proceedings of the 7th International Conference on Machine Learning,2000, pp. 359-366.
[17]Yu L., Liu H. Redundancy based feature selection for microarray data [C]. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, pp.737-742.
[18]刘峤.基于最短描述长度的高维特征选择方法研究[D].成都：电子科技大学信息安全专业,2010.
[19]陈友,程学旗,李洋等.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7)：1639-1651.
[20]Narendra P.M., Fukunaga K. A branch and bound algorithm for feature subset selection [J]. IEEE Transactions on Computers,1977,100(9):917-922.
[21]Davies S., Russell S. NP-completeness of searches for smallest possible feature sets [C].In Proceedings of the AAAI Fall'94 Symposium on Relevance, New Orleans,1994, pp.37-39.
[22]Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(4):491-502.
[23]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.
[24]Dash M., Liu H. Feature selection for classification [J]. Intelligent data analysis,1997,1(3): 131-156.
[25]Molina L.C., Belanche L., Nebot A. Feature selection algorithms:A survey and experimental evaluation [C]. Li Proceedings of the 2nd IEEE International Conference on Data Mining, 2002, pp.306-313.
[26]Yu L., Liu H. Feature selection for high-dimensional data:A fast correlation-based filter solution [C].In Proceedings of the 20th International Conference on Machine Learning,2003, pp.856-863.
[27]Peng H.C., Long F., Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.
[28]Dash M., Liu H. Consistency-based search in feature selection [J]. Artificial intelligence,2003, 151(1):155-176.
[29]Almuallim H., Dietterich T.G. Learning boolean concepts in the presence of many irrelevant features [J]. Artificial Intelligence,1994,69(1):279-305.
[30]Dash M. Feature selection via set cover [C].In Proceedings of IEEE Knowledge and Data Engineering Exchange Workshop,1997, pp.165-171.
[31]Li L.P., Weinberg C.R., Darden T.A., Pedersen L.G. Gene selection for sample classification based on gene expression data:study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics,2001,17(12):1131-1142.
[32]Chen X.W. Margin-based wrapper methods for gene identification using microarray. Neurocomputing,2006,69(16-18):2236-2243.
[33]Akaike H. Information theory and an extension of the maximum likelihood principle [C]. In Second International Symposium on Information Theory. Akademinai Kiado,1973, pp. 267-281.
[34]Rissanen J. Modeling by shortest data description [J]. Automatica,1978,14(5):465-471.
[35]Blum A.L., Langley P. Selection of relevant features and examples in machine learning [J]. Artificial intelligence,1997,97(1):245-271.
[36]Kohavi R., John G.H. Wrappers for feature subset selection [J]. Artificial intelligence,1997, 97(1):273-324.
[37]Guyon I., Elisseeff A. An introduction to variable and feature selection [J]. The Journal of Machine Learning Research,2003,3:1157-1182.
[38]Smialowski P.. Frishman D., Kramer S. Pitfalls of supervised feature selection [J]. Bioinformatics,2010,26(3):440-443.
[39]Forman G. An extensive empirical study of feature selection metrics for text classification [J]. The Journal of machine learning research,2003,3:1289-1305.
[40]Mitra P., Murthy C.A., Pal S.K. Unsupervised feature selection using feature similarity [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(3):301-312.
[41]Dy J.G., Brodley C.E. Feature selection for unsupervised learning [J]. The Journal of Machine Learning Research,2004,5:845-889.
[42]Zhao J.D., Lu K., He X.F. Locality sensitive semi-supervised feature selection [J]. Neurocomputing,2008,71(10):1842-1849.
[43]Zhao Z., Liu H. Semi-supervised Feature Selection via Spectral Analysis [C].In Proceedings of the 7th SIAM International Conference on Data Mining,2007, pp.641-646.
[44]Song Y.Q., Nie F.P., Zhang C.S., Xiang S.M. A unified framework for semi-supervised dimensionality reduction [J]. Pattern Recognition,2008,41(9):2789-2799.
[45]Wang Y.H., Makedon F.S., Ford J.C., Pearlman J. HykGene:a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data [J]. Bioinformatics,2005,21(8):1530-1537.
[46]Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring [J]. Science,1999,286(5439):531-537.
[47]Cheng Q., Zhou H.B., Cheng J. The Fisher-Markov selector:fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(6): 1217-1233.
[48]Song L., Smola A., Gretton A., Bedo J., Borgwardt K. Feature selection via dependence maximization [J]. The Journal of Machine Learning Research,2012,98888(1):1393-1434.
[49]Hsu W.H. Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning [J]. Information Sciences,2004,163(1): 103-122.
[50]Chuang L.Y., Yang C.H., Li J.C., Yang C.H. A hybrid BPSO-CGA approach for gene selection and classification of microarray data [J]. Journal of Computational Biology,2012,19(1): 68-82.
[51]Rodrigues D., Pereira L.A.M., Nakamura R.Y.M., Costa K.A.P., Yang X.S., Souza A.N., Papa J.P. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest [J]. Expert Systems with Applications,2014,41(5):2250-2258.
[52]Papa J.P., Falcao A.X., De Albuquerque V.H.C., Tavares J.M.R.S. Efficient supervised optimum-path forest classification for large datasets [J]. Pattern Recognition,2012,45(1): 512-520.
[53]Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines [J]. Machine learning,2002,46(1-3):389-422.
[54]Tan M., Wang L., Tsang I.W. Learning sparse svm for feature selection on very high dimensional datasets [C]. In Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010, pp.1047-1054.
[55]Ma S.G., Song X., Huang J. Supervised group Lasso with applications to microarray data analysis [J]. BMC Bioinformatics,2007,8:60.
[56]Maldonado S., Weber R., Basak J. Simultaneous feature selection and classification using kernel-penalized support vector machines [J]. Information Sciences,2011,181(1):115-128.
[57]Yang P.Y., Zhou B.B., Zhang Z.L., Zomaya A.Y. A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data [J]. BMC bioinformatics,2010, 11(Suppl 1):S5.
[58]Sebban M., Nock R. A hybrid filter/wrapper approach of feature selection using information theory [J]. Pattern Recognition,2002,35(4):835-846.
[59]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.
[60]王树林,王戟,陈火旺,李树涛,张波云.肿瘤信息基因启发式宽度优先搜索算法研究[J].计算机学报,2008,31(4)：636-649.
[61]Akadi A.E., Amine A, Ouardighi A.E., Aboutajdine D. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper [J]. Knowledge and Information Systems,2011,26(3): 487-500.
[62]Cadenas J.M., Garrido M.C., MartiNez R. Feature subset selection Filter-Wrapper based on low quality data [J]. Expert Systems with Applications,2013,40(16):6241-6252.
[63]Loscalzo S., Yu L., Ding C. Consensus group stable feature selection [C]. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, pp.567-576.
[64]Kalousis A., Prados J., Hilario M. Stability of feature selection algorithms:a study on high-dimensional spaces [J]. Knowledge and information systems,2007,12(1):95-116.
[65]Han Y., Yu L. A variance reduction framework for stable feature selection [J]. Statistical Analysis and Data Mining,2012,5(5):428-445.
[66]Saeys Y., Abeel T., Van de P.Y. Robust feature selection using ensemble feature selection techniques [M]. Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg,2008, pp.313-325.
[67]Davis C.A., Gerick F., Hintermair V., Friedel C.C., Fundel K., Kuffner R., Zimmer R. Reliable gene signatures for microarray classification:assessment of stability and performance [J]. Bioinformatics,2006,22(19):2356-2363.
[68]Ein-Dor L., Kela I., Getz G., Givol D., Domany E. Outcome signature genes in breast cancer: is there a unique set? [J]. Bioinformatics,2005,21(2):171-178.
[69]Awada W., Khoshgoftaar T.M., Dittman D., Wald R., Napolitano A. A review of the stability of feature selection techniques for bioinformatics data [C]. In Proceedings of the 13th International Conference on Information Reuse and Integration,2012, pp.356-363.
[70]Yu L., Han Y, Berens M.E. Stable gene selection from microarray data via sample weighting [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics,2012,9(1): 262-272.
[71]Hassan M.R., Hossain M.M., Bailey J., Macintyre G, Ho J.W., Ramamohanarao K. A voting approach to identify a small number of highly predictive genes using multiple classifiers [J]. BMC bioinformatics,2009,10(Suppl 1):S19.
[72]Li X., Rao S.Q., Wang Y.D., Gong B.S. Gene mining:a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling [J]. Nucleic Acids Research,2004,32(9):2685-2694.
[73]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.
[74]Brock G.N., Shaffer J.R., Blakesley R.E., Lotz M.J., Tseng G.C. Which missing value imputation method to use in expression profiles:a comparative study and two selection schemes [J]. BMC Bioinformatics,2008,9:12.
[75]李颖新,李建更,阮晓钢.肿瘤基因表达谱分类特征基因选取问题及分析方法研究[J].计算机学报,2006,29(2)：324-330.
[76]Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics [J]. Bioinformatics,2007,23(19):2507-2517.
[77]Ramon D.U., Sara A.A. Gene selection and classification of microarray data using random forest [J]. BMC Bioinformatics,2006,7:3.
[78]李瑶.基因芯片数据分析与处理[M].北京：化学工业出版社,2006.
[79]于化龙.基于DNA微阵列数据的癌症分类技术研究[D].哈尔滨：哈尔滨工程大学计算机应用技术专业,2010.
[80]Tibshirani R. Regression shrinkage and selection via the Lasso [J]. Journal of the Royal Statistical Society Series B-Methodological,1996,58(1):267-288.
[81]Efron B., Hastie T., Johnstone I., Tibshirani R. Least Angle Regression [J]. Journal of the Institute of Mathematical Statistics,2004,32(2):407-499:
[82]施万锋,胡学钢,俞奎.一种面向高维数据的均分式Lasso特征选择方法[J].计算机工程与应用,2012,48(1)：157-161.
[83]Singh D., Febbo P.G, Ross K., Jackson D.G., Manola J., Ladd C., et al. Gene expression correlates of clinical prostate cancer behavior [J]. Cancer Cell,2002,1(2):203-209.
[84]Zhao Y.D., Simon R. BRB ArrayTools data archive for human cancer gene expression:a unique and efficient data sharing resource [J]. Cancer Informatics,2008,6:9-15.
[85]Brown M.P.S., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Furey T.S., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines [J]. Proceedings of the National Academy of Sciences of the United States of America.2000,97(1):262-267.
[86]George G.V.S., Raj V.C. Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profiles [J]. International Journal of Computer Science and Engineering Survey,2011,2(3):16-27.
[87]Huang D.S., Ip H.H.S., Law K.C.K., Chi Z. Zeroing polynomials using modified constrained neural network approach [J]. IEEE Transactions on Neural Networks,2005,16(3):721-732.
[88]Huang D.S., Zheng C.H. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data [J]. Bioinformatics,2006,22(15): 1855-1862.
[89]Wang S.L., Zhu Y.H., Jia W., Huang D.S. Robust classification method of tumor subtype by using correlation filters [J]. Journal of IEEE/ACM Transactions on Computational Biology and Bioinformatics,2012,9(2):580-591.
[90]Robnik-Sikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RreliefF [J]. Machine learning,2003,53(1-2):23-69.
[91]Hanczar B., Courtine M., Benis A., Hennegar C, Clement K., Zucker J.D. Improving classification of microarray data using prototype-based feature selection [J]. ACM SIGKDD Explorations Newsletter,2003,5(2):23-30.
[92]Tan F., Fu X.Z., Wang H., Zhang Y.Q., Bourgeois A. A hybrid feature selection approach for microarray gene expression data [C]. In Proceedings of the 6th International Conference on Computational Science,2006, pp.678-685.
[93]Zheng S.F., Liu W.X. An experimental comparison of gene selction by Lasso and Dantzig selector for cancer classification [J]. Computers in Biology and Medicine,2011,41(11): 1033-1040.
[94]Liu H., Motoda H., Setiono R., Zhao Z. Feature selection:an ever evolving frontier in data mining [C]. In Proceedings of the 4th Workshop on Feature Selection in Data Mining,2010, pp.4-13.
[95]Alon U., Barkai N., Notterman D.A., Gish S., Ybarra S., Mack D., et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [C]. In Proceedings of the National Academy of Sciences of the United States of America,1999,96(12):6745-6750.
[96]Shipp M.A., Ross K.N., Tamayo P., Weng A.P., Kutok J.L., Aguiar R.C.T., et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning [J]. Nature Medicine,2002,8(1):68-74.
[97]Gordon G.J., Jensen R.V., Hsiao L.L., Gullans S.R., Blumenstock J.E., Ramaswamy S., et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma [J]. Cancer Research,2002,62:4963.
[98]Frank E., Hall M., Trigg L., Holmes G., Witten I.H. Data mining in bioinformatics using Weka [J]. Bioinformatics,2004,20(15):2479-2481.
[99]Kulkarni A., Kumar N., Ravi V., Murthy U.S. Colon cancer prediction with genetics profiles using evolutionary techniques [J]. Expert Systems with Applications,2011,38(3):2752-2757'.
[100]Shen Q., Shi W.M., Kong W., Ye B.X. A combination of modified particleswarm optimization algorithm and support vector machine for gene selection and tumor classification [J]. Talanta, 2007,71(4):1679-1683.
[101]Abeel T., Helleputte T., Van de Peer Y., Dupont P., Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods [J]. Bioinformatics,2010,26(3): 392-398.
[102]Hilario M., Kalousis A. Approaches to dimensionality reduction in proteomic biomarker studies [J]. Briefings in Bioinformatics,2008,9(2):102-118.
[103]Nam D., Kim S.Y. Gene-set approach for expression pattern analysis [J]. Briefings in bioinformatics,2008,9(3):189-197.
[104]Zhou X., Tuck D.P. MSVM-RFE:extensions of SVM-RFE for multiclass gene selection on DNA microarray data.[J]. Bioinformatics,2007,23(9):1106-1114.
[105]Wang L., Zhu J., Zou H. Hybrid huberized support vector machines for microarray classification and gene selection [J]. Bioinformatics,2008,24(3):412-419.
[106]Kursa M.B. Robustness of Random Forest-based gene selection methods [J]. BMC bioinformatics,2014,15(1):8.
[107]Zhai Y.T., Tan M.K., Tsang I.W., Ong Y.S. Discovering support and affiliated features from very high dimensions [C]. In Proceedings of the 29th International Conference on Machine Learning,2012.
[108]Yu L., Ding C, Loscalzo S. Stable feature selection via dense feature groups [C].In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2008, pp.803-811.
[109]Hong J.H., Cho S.B. The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming [J]. Artificial Intelligence in Medicine,2006,36(1): 43-58.
[110]Cho S.B., Won H.H. Cancer classification using ensemble of neural networks with multiple significant gene subsets [J]. Applied Intelligence,2007,26(3):243-250.
[111]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.
[112]Dietterich T.G. Ensemble methods in machine learning [M]. Multiple classifier systems. Springer Berlin Heidelberg,2000:1-15.
[113]Breiman L. Bagging predictors [J]. Machine learning,1996,24(2):123-140.
[114]Schapire R.E. The strength of weak learnability [J]. Machine learning,1990,5(2):197-227.
[115]王清.集成学习中若干关键问题的研究[D].上海：复旦大学计算机软件与理论专业,2011.
[116]He Z.Y., Yu W.C. Stable feature selection for biomarker discovery [J]. Computational biology and chemistry,2010,34(4):215-225.
[117]Moon H., Ahn H., Kodell R.L., Baek S., Lin C.J., Chen J.J. Ensemble methods for classification of patients for personalized medicine with high-dimensional data [J]. Artificial intelligence in medicine,2007,41(3):197-207.
[118]Dutkowski J., Gambin A. On consensus biomarker selection [J]. BMC bioinformatics,2007, 8(Suppl 5):S5.
[119]Duda R.O., Hart P.E., Stork D.G Pattern classification [M]. John Wiley & Sons, Inc,1995.
[120]Liu H.W., Liu L., Zhang H.J. Feature selection using mutual information:an experimental study [J]. In Proceedings of the 10th Pacific Rim International Conference on Attificial Intelligence,2008, pp.235-246.