Optimal combination of feature selection and classification via local hyperplane based learning strategy

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Cheng, Xiaoping ^a   Cai, Hongmin ^a   Zhang, Yue ^b,c   Xu, Bo ^a   Su, Weifeng ^c  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b JINAN UNIVERSITY   (China)

^c BNU HKBU UNITED INTERNATIONAL COLLEGE   (China)

Author keywords

Classification; Feature weighting; HKNN; Local hyperplane; Local learning

Indexed keywords

BENCHMARKING; DECISION TREES; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; GENES; GEOMETRY; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; FEATURE SELECTION AND CLASSIFICATION; FEATURE SELECTION METHODS; FEATURE WEIGHTING; HKNN; K NEAREST NEIGHBOR (KNN); LOCAL HYPERPLANES; LOCAL LEARNING;

CLASSIFICATION (OF INFORMATION);

CLASSIFICATION; CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; HUMAN; MACHINE LEARNING; NEOPLASM; STANDARDS; SUPPORT VECTOR MACHINE;

CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; MACHINE LEARNING; NEOPLASMS; SUPPORT VECTOR MACHINE;

EID: 85019235741     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0629-6     Document Type: Article

References (28)

1. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression In: Randy S, editor. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences Press: 2004. p. 9309-9314.
2. Chang HY, Nuyten DSA, Sneddon JB, Hastie T, Tibshirani R, Sørlie T. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival In: Randy S, editor. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences Press: 2005. p. 3738-3743.
3. Yang K, Cai Z, Li J, Lin G. A stable gene selection in microarray data analysis. BMC Bioinformatics. 2006; 7:228-235.
4. Ni B, Liu J. A hybrid filter/wrapper gene selection method for microarray classification In: Daniel Y, Xizhao W, Jianbo S, editors. Proceedings of 2004 International Conference on Machine Learning and Cybernetics. IEEE Press: 2004. p. 2537-2542.
5. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007; 23:2507-2517.
6. Abdi H, Williams LJ. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics. 2010; 2(4):433-459.
7. Pok G, Liu Steve J-C, Ryu KH. Effective feature selection framework for cluster analysis of microarray data. Bioinformatics. 2010; 4:385-392.
8. Talavera L. An evaluation of filter and wrapper methods for feature selection in categorical clustering In: Famili A, editor. Advances in Intelligent Data Analysis VI. Berlin Heidelberg Press: 2005. p. 440-451.
9. Sun Y. Iterative relief for feature weighting: algorithms, theories, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007; 29:1035-1051.
10. Brown G. Some thoughts at the interface of ensemble methods and feature selection In: Neamat EG, Josef K, Fabio R, editors. Multiple Classifier Systems. Springer Press: 2010. p. 314-314.
11. Kim Y, Street WN, Menczer F. Efficient dimensionality reduction approaches for feature selection In: Arivazhagan S, editor. International Conference on Conference on Computational Intelligence and Multimedia Applications. IEEE Press: 2007. p. 121-127.
12. He X, Yan S, Hu Y, Niyogi P, Zhang H-J. Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27:328-340.
13. Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science. 2000; 290:2323-2326.
14. Yan S, Xu D, Zhang B, Zhang H-J, Yang Q, Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Int. 2007; 29:40-51.
15. Kim T-K, Kittler J. Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27:318-327.
16. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995; 3:273-297.
17. Yang J Zhang, Yang J-y, Zhang D. From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis. Pattern Recognition. 2011; 44:1387-1402.
18. Villegas M, Paredes R. Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters. 2011; 32:633-639.
19. Villegas M, Paredes R. Simultaneous learning of a discriminative projection and prototypes for nearest-neighbor classification. IEEE Conference on Computer Vision and Pattern Recognition. 2008:1-8.
20. Vincent P, Bengio Y. K-local hyperplane and convex distance nearest neighbor algorithms In: Thomas G, Sue B, Zoubin G, editors. Advances in Neural Information Processing Systems. MIT Press: 2001. p. 985-992.
21. Kim T-K, Kittler J. UCI machine learning repository. University of California Irvine School of Information Andcomputer Sciences. 2007.
22. Aha DW, Kibler D, Albert MK. Instance-based learning algorithms. Machine Learning. 1991; 1:37-66.
23. Cai D, He X, Zhou K, Han J, Bao H. Locality sensitive discriminant analysis In: Veloso M, editor. Proceedings of the 20th International Joint Conference on Artificial Intelligence. MIT Press: 2007. p. 708-713.
24. Sun Y, Todorovic S, Goodison S. Local-learning-based feature selection for high-dimensional data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010; 32:1610-1626.
25. Cai H, Ng M. Feature weighting by relief based on local hyperplane approximation In: Pang-Ning T, editor. Advances in Knowledge Discovery and Data Mining. Springer Press: 2012. p. 335-346.
26. Duan KB, Rajapakse JC, Wang H, Azuaje F. Multiple svm-rfe for gene selection in cancer classification with expression data. IEEE Transactions on NanoBioscience. 2005; 4:228-234.
27. Liaw A, Wiener M. Classification and regression by randomforest. R news. 2002; 2:18-22.
28. Meier L, Van De Geer S, Bühlmann P. The group lasso for logistic regression. J R Stat Soc Series B (Statistical Methodology). 2008; 70:53-71.