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Issue No.04 - July/August (2011 vol.8)
pp: 1080-1092
Feng Yang , Nanyang Technological University, Singapore
K.Z. Mao , Nanyang Technological University, Singapore
Feature selection often aims to select a compact feature subset to build a pattern classifier with reduced complexity, so as to achieve improved classification performance. From the perspective of pattern analysis, producing stable or robust solution is also a desired property of a feature selection algorithm. However, the issue of robustness is often overlooked in feature selection. In this study, we analyze the robustness issue existing in feature selection for high-dimensional and small-sized gene-expression data, and propose to improve robustness of feature selection algorithm by using multiple feature selection evaluation criteria. Based on this idea, a multicriterion fusion-based recursive feature elimination (MCF-RFE) algorithm is developed with the goal of improving both classification performance and stability of feature selection results. Experimental studies on five gene-expression data sets show that the MCF-RFE algorithm outperforms the commonly used benchmark feature selection algorithm SVM-RFE.
Feature selection, multicriterion fusion, recursive feature elimination, robustness, classification.
Feng Yang, K.Z. Mao, "Robust Feature Selection for Microarray Data Based on Multicriterion Fusion", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 4, pp. 1080-1092, July/August 2011, doi:10.1109/TCBB.2010.103
[1] F.K. Ahmad, N.M. Norwawi, S. Deris, and N.H. Othman, “A Review of Feature Selection Techniques via Gene Expression Profiles,” Proc. Int'l Symp. Information Technology (ITSim '08), pp. 1-7, 2008.
[2] U. Alon, N. Barkai, D.A. Notterman, K. Gishdagger, S. Ybarradagger, D. Mackdagger, and A.J. Levine, “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays,” Proc. Nat'l Academy of Sciences USA, vol. 96, no. 12, pp. 6745-6750, June 1999.
[3] G. Bontempi, “A Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 2, pp. 293-300, Apr. 2007.
[4] A.P. Bradley, “The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
[5] U. Braga-Neto and E.R. Dougherty, “Is Cross-Validation Valid for Small-Sample Microarray Classification?” Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004.
[6] C.C. Chang and C.J. Lin, “LIBSVM : A Library for Support Vector Machines,”, 2001.
[7] S.M. Chao and E.R. Dougherty, “The Peaking Phenomenon in the Presence of Feature-Selection,” Pattern Recoginition Letters, vol. 29, no. 11, pp. 1667-1674, 2008.
[8] M.R. Chernick, Bootstrap Methods: A Guide for Practitioners and Researchers, second ed., John Wiley & Sons, 2007.
[9] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, Sept. 1995.
[10] T.G. Dietterich, “Machine Learning Research: Four Current Directions,” Artificial Intelligence Magazine, vol. 18, no. 4, pp. 97-136, 1997.
[11] T.G. Dietterich, “Ensemble Methods in Machine Learning,” Proc. First Int'l Workshop Multiple Classifier Systems (MCS '00), vol. 1857, pp. 1-15, 2000.
[12] C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank Aggregation Revisited,” Proc. Int'l World Wide Web Conf., May 2001.
[13] T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, and D. Haussler, “Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data,” Bioinformatics, vol. 16, no. 10, pp. 906-914, Oct. 2000.
[14] C. Furlanello, M. Serafini, S. Merler, and G. Jurman, “Semisupervised Learning for Molecular Profiling,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 2, no. 2, pp. 110-118, Oct. 2005.
[15] T. Golub et al., “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, vol. 286, no. 5439, pp. 531-537, Oct. 1999.
[16] I. Guyon, NIPS2001/, 2009.
[17] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene Selection for Cancer Classification Using Support Vector Machines,” Machine Learning, vol. 46, nos. 1-3, pp. 389-422, Jan. 2002.
[18] I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” J. Machine Learning Research, vol. 3, pp. 1157-1182, Mar. 2003.
[19] G. Gulgezen, Z. Cataltepe, and L. Yu, “Stable and Accurate Feature Selection,” Proc. European Conf. Machine Learning and Knowledge Discovery in Databases: Part I (ECML PKDD '09), vol. 5781, pp. 455-468, 2009.
[20] D.F. Hsu and T. Isak, “Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval,” Information Retrieval, vol. 8, no. 3, pp. 449-480, Jan. 2005.
[21] J. Hua, Z.X. Xiong, J. Lowey, E. Suh, and E.R. Dougherty, “Optimal Number of Features as a Function of Sample Size for Various Classification Rules,” Bioinformatics, vol. 21, no. 8, pp. 1509-1515, 2005.
[22] D. Huang and T.W.S. Chow, “Effective Gene Selection Method with Small Sample Sets Using Gradient-Based and Point Injection Techniques,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 3, pp. 467-475, July-Sept. 2007.
[23] A. Kalousis, J. Prados, and M. Hilario, “Stability of Feature Selection Algorithms: A Study on High-Dimensional Spaces,” Knowledge and Information Systems, vol. 12, no. 1, pp. 95-116, May 2007.
[24] E. Kim and J. Ko, “Dynamic Classifier Integration Method,” Proc. Int'l Workshop Multiple Classifier Systems (MCS '05), pp. 97-107, 2005.
[25] A. Klementiev, D. Roth, and K. Small, “An Unsupervised Learning Algorithm for Rank Aggregation,” Proc. European Conf. Machine Learning (ECML '07), pp. 616-623, 2007.
[26] R. Kohavi and G.H. John, “Wrapper for Feature Subset Selection,” Artificial Intelligence, vol. 97, nos. 1-2, pp. 273-324, Dec. 1997.
[27] P. Krizek, J. Kittler, and V. Hlavac, “Improving Stability of Feature Selection Methods,” Proc. 12th Int'l Conf. Computer Analysis of Images and Patterns (CAIP'07) , vol. 4673, pp. 929-936, 2007.
[28] Y. Leung and Y. Hung, “A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 108-117, Jan.-Mar. 2010.
[29] Y.T. Liu, T.Y. Liu, T. Qin, Z.M. Ma, and H. Li, “Supervised Rank Aggregation,” Proc. Int'l Conf. World Wide Web, pp. 481-490, 2007.
[30] S. Loscalzo, L. Yu, and C. Ding, “Consensus Group Stable Feature Selection,” Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD '09), pp. 567-576, 2009.
[31] S.L. Pomeroy et al., “Prediction of Central Nervous System Embryonal Tumor Outcome Based on Gene Expression,” Nature, vol. 415, pp. 265-271, 2002.
[32] M. Robnik-Sikonja and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF,” Machine Learning, vol. 53, pp. 23-69, 2003.
[33] Y. Saeys, I. Inza, and P. Larranaga, “A Review of Feature Selection Techniques in Bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
[34] Y. Saeys, T. Abeel, and Y. Van de Peer, “Robust Feature Selection Using Ensemble Feature Selection Techniques,” Proc. European Conf. Machine Learning and Knowledge Discovery, pp. 313-25, 2008.
[35] M.A. Shipp et al., “Diffuse Large B-Cell Lymphoma Outcome Prediction by Gene-Expression Profiling and Supervised Machine Learning,” Nature Medicine, vol. 8, no. 1, pp. 68-74, Jan. 2002.
[36] D.V. Shridhar, E.B. Bartlett, and R.C. Seagrave, “Information Theoretic Subset Selection for Neural Network Models,” Computers and Chemical Eng., vol. 22, nos. 4-5, pp. 613-626, 1998.
[37] D. Singh et al., “Gene Expression Correlates of Clinical Prostate Cancer Behavior,” Cancer Cell, vol. 1, no. 2, pp. 203-209, 2002.
[38] P. Somol and J. Novovicova, “Evaluating the Stability of Feature Selectors that Optimize Feature Subset Cardinality,” Proc. Int'l Workshop Structural, Syntactic, and Statistical Pattern Recognition, pp. 956-966, 2008.
[39] Y.C. Tang, Y.Q. Zhang, and Z. Huang, “Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 3, pp. 365-381, July-Sept. 2007.
[40] K. Tumer and J. Ghosh, Theoretical Foundations of Linear and Order Statistics Combiners for Neural Pattern Classifiers, Technical Report 95-02-98, The Computer and Vision Research Center, Univ. of Texas, 1998.
[41] M. van Erp and L. Schomaker, “Variants of the Borda Count Method for Combining Ranked Classifier Hypotheses,” Proc. Int'l Workshop Frontiers in Handwriting Recognition, pp. 443-452, 2000.
[42] V. Vapnik, Statistical Learning Theory. John Wiley & Sons, 1998.
[43] Wikipedia., 2009.
[44] L. Yu, C. Ding, and S. Loscalzo, “Stable Feature Selection via Dense Feature Groups,” Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD '08), pp. 803-811, 2008.
[45] X. Zhou and K.Z. Mao, “The Ties Problem Resulting from Counting-Based Error Estimators and Its Impact on Gene Selection Algorithms,” Bioinformatics, vol. 22, no. 20, pp. 2507-2515, 2006.
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