de Sa Marques J P. Pattern Recognition Concepts, Methods and Applications. Berlin, Germany: SpringerVerlag, 2002
[2]
Ganeshanandam S, Krzanowski W J. On Selecting Variables and Assessing Their Performance in Linear Discriminant Analysis. Australian Journal of Statistics, 1989, 31(3): 433447
[3]
Bian Zhaoqi, Zhang Xuegong. Pattern Recognition. 2nd Edition. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (边肇祺,张学工.模式识别.第2版.北京:清华大学出版社, 2000)
[4]
Theodoridis S, Koutroumbas K. Pattern Recognition. 2nd Edition. New York, USA: Elsevier, 2003
[5]
Dougherty E R. Small Sample Issues for MicroarrayBased Classification. Comparative and Functional Genomics, 2001, 2(1): 2834
[6]
Dougherty E R, Shmulevich I, Bittner M L. Genomic Signal Processing: The Salient Issues. EURASIP Journal on Applied Signal Processing, 2004, 4(1): 146153
[7]
Kim S, Dougherty E R, Barrera J, et al. Strong Feature Sets from Small Samples. Journal of Computational Biology, 2002, 9(1): 127146
[8]
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, USA: SpringerVerlag, 2001
[9]
Webb R A. Statistical Pattern Recognition. New York, USA: John Wiley & Son, 2002
[10]
Dudoit S, Fridlyand J, Speed T P. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association, 2002, 97(457): 7787
[11]
Hamamoto Y, Uchimura S, Matsuura Y, et al. Evaluation of the Branch and Bound Algorithm for Feature Selection. Pattern Recognition Letters, 1990, 11(7): 453456
[12]
Wang Ling. Intelligent Optimization Algorithms with Applications. Beijing, China: Tsinghua University Press, 2004 (in Chinese) (王 凌.智能优化算法及其应用.北京:清华大学出版社, 2004)
[13]
Tsymbal A, Puuronen S. Ensemble Feature Selection with the Simple Bayesian Classification. Information Fusion, 2003, 4(2): 87100
[14]
Wu B L, Abbott T, Fishman D,et al. Comparison of Statistical Methods for Classification of Ovarian Cancer Using Mass Spectrometry Data. Bioinformatics, 2003, 19(13): 16361643
[15]
Yang J, Honavar V. Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems, 1998, 13(2): 4449
[16]
Chiang L H, Pell R J. Genetic Algorithms Combined with Discriminant Analysis for Key Variable Identification. Journal of Process Control, 2004, 14(2): 143155
[17]
Siedlecki W, Sklansky J. A Note on Genetic Algorithms for Large Scale Feature Selection. Pattern Recognition Letters, 1989, 10(11): 335347
[18]
Peng Sihua, Xu Qianghua, Ling Xuefeng. Molecular Classification of Cancer Types from Microarray Data Using the Combination of Genetic Algorithms and Support Vector Machines. FEBS Letters, 2003, 555(2): 358362
[19]
Mao K Z. Fast Orthogonal Forward Selection Algorithm for Feature Subset Selection. IEEE Trans on Neural Networks, 2002, 13(5): 12181224
[20]
Furlanello C, Serafini M, Merler S, et al. An Accelerated Procedure for Recursive Feature Ranking on Microarray Data. Neural Networks, 2003, 16(5/6): 641648
Pudil P, Novovicova J, Kittler J. Floating Search Methods in Feature Selection. Pattern Recognition Letters, 1994, 15(11): 11191125
[23]
Inza I, Larranaga P, Blanco R, et al. Filter Versus Wrapper Gene Selection Approaches in DNA Microarray Domains. Artificial Intelligence in Medicine, 2004, 31(2): 91103
[24]
Zhou Xiaobo, Wang Xiaodong, Dougherty E R. NonlinearProbit Gene Classification Using MutualInformation and WaveletBased Feature Selection. Biological Systems, 2004, 12(3): 371386
[25]
Furey T S, Cristianini N, Duffy N, et al. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics, 2000, 16(10): 906914
[26]
Zhou Xiaobo, Wang Xiaodong, Dougherty E R. Gene Selection Using Logistic Regressions Based on AIC, BIC and MDL Criteria. Journal of New Mathematics and Natural Computation, 2005, 1(1): 129145
[27]
Zhou Xiaobo, Wang Xiaodong, Dougherty E R. Construction of Genomic Networks Using Mutual Information Clustering and ReversibleJump Markov Chain Monte Carlo Predictor Design. Signal Processing, 2003, 83(4): 745761
[28]
Tabus I, Astola J. On the Use of MDL Principle in Gene Expression Prediction. EURASIP Journal of Applied Signal Processing, 2001, 4: 297303
[29]
Liu Huiqing, Li Jinyan, Wong L. A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns. Genome Informatics, 2002, 13: 5160
[30]
Michael M, Lin W C. Experimental Study of Information Measure and InterIntra Class Distance Ratios on Feature Selection and Orderings. IEEE Trans on System, Man, and Cybernetics, 1973, 3(2): 172181
[31]
Sindhwani V, Rakshit S, Deodhare D, et al. Feature Selection in MLPs and SVMs Based on Maximum Output Information. IEEE Trans on Neural Networks, 2004, 15(4): 937948
[32]
Haering N, Lobo N D V. Feature and Classification Methods to Locate Deciduous Trees in Images. Computer Vision and Image Understanding, 1999, 75(1/2): 133149
[33]
Hsu W H. Genetic Wrappers for Feature Selection in Decision Tree Induction and Variable Ordering in Bayesian Network Structure Learning. Information Sciences, 2004, 163(1/2/3): 103122
[34]
Li L, Weinberg C R, Darden T A, et al. 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): 11311142
[35]
Shima K, Todoriki M, Suzuki A. SVMBased Feature Selection of Latent Semantic Features. Pattern Recognition Letters, 2004, 25(9): 10511057
[36]
Jack L B, Nandi A K. Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms. Mechanical Systems and Signal Processing, 2002, 16(2/3): 373390
[37]
Verikas A, Bacauskiene M. Feature Selection with Neural Networks. Pattern Recognition Letters, 2002, 23(11): 13231335
Guyon I, Weston J, Barnhill S, et al. Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning, 2002, 46(1/2/3): 389422
[40]
Weston J, Mukherjee S, Chapelle O, et al. Feature Selection for SVMs // Solla S A, Leen T K, Muller K R, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001, 13: 668674
[41]
Perner P, Apte C. Empirical Evaluation of Feature Subset Selection Based on a RealWorld Data Set // Proc of the 4th European Conference on Principles of Data Mining and Knowledge Discovery. London, UK: SpringerVerlag, 2000: 575580
[42]
Zhang Xuegong, Wong W H. Recursive Sample Classification and Gene Selection Based on SVM: Method and Software Description. Technical Report, Boston, USA: Harvard School of Public Health. Department of Biostatistics, 2001
[43]
Brown M P S, Grundy W N, Lin D, et al. KnowledgeBased Analysis of Microarray Gene Expression Data by Using Support Vector Machines. Proc of the National Academy of Science, 2000, 97(1): 262267
[44]
Barzilay O, Brailovsky V L. On Domain Knowledge and Feature Selection Using a Support Vector Machine. Pattern Recognition Letters, 1999, 20(5): 475484
[45]
Fortuna J, Capson D. Improved Support Vector Classification Using PCA and ICA Feature Space Modification. Pattern Recognition, 2004, 37(6): 11171129
[46]
Simek K, Fujarewicz K, Swierniak A, et al. Using SVD and SVM Methods for Selection, Classification, Clustering and Modeling of DNA Microarray Data. Engineering Applications of Artificial Intelligence, 2004, 17(4): 417427
[47]
Fujarewicz K, Wiench M. Selecting Differentially Expressed Genes for Colon Tumor Classification. International Journal of Applied Mathematics and Computer Science, 2003, 13(3): 327335
[48]
Mao Yong, Pi Daoying, Yu Ming, et al. Accelerated Recursive Feature Elimination by Support Vector Machine for Key Variable Identification. Chinese Journal of Chemical Engineering, 2006, 14(1): 6572
[49]
Adam B L, Vlahou A, Semmes O J, et al. Proteomic Approaches to Biomarker Discovery in Prostate and Bladder Cancers. Proteomics, 2001, 1(10): 12641270
[50]
Sun Z H, Bebis G, Miller R. Object Detection Using Feature Subset Selection. Pattern Recognition, 2004, 37(11): 21652176
[51]
Jain A K, Duin R D W, Mao J C. Statistical Pattern Recognition: A Review. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(1): 437
[52]
Kudo M, Sklansky J. Comparison of Algorithms That Select Features for Pattern Classifiers. Pattern Recognition, 2000, 33(1): 2541
[53]
Chen Xuewen. An Improved Branch and Bound Algorithm for Feature Selection. Pattern Recognition Letters, 2003, 24(12): 19251933
[54]
Fukunaga K, Narendra P M. A Branch and Bound Algorithm for Computing kNearest Neighbors. IEEE Trans on Computers, 1975, 24(7): 750753
[55]
Mao Yong, Zhou Xiaobo, Pi Daoying, et al. Parameters Selection in Gene Selection Using Gaussian Kernel Support Vector Machines by Genetic Algorithm. Journal of Zhejiang University: Science B, 2005, 6(10): 961973
[56]
Li Fan, Yang Yiming. Using Recursive Classification to Discover Predictive Features // Proc of the ACM Symposium on Applied Computing. Santa Fe, New Mexico, 2005: 10541058
[57]
Mao Yong, Zhou Xiaobo, Yin Zheng, et al. Gene Selection Using Recursive Feature Elimination Based on Gaussian Kernel Support Vector Machine with Adaptive Kernel Width Strategy // Proc of the 1st International Conference on Rough Sets and Knowledge Technology. Chongqing, China, 2006: 799806
