[1]
J. Fan; J. Lv; “A selective overview of variable selection in high dimensional feature space”, Statistica Sinica, Vol. 20(1), pp. 101-148, 2010.
[2]
M. Pal; G. M. Foody; “Feature Selection for Classification of Hyperspectral Data by SVM”, IEEE Trans. Geoscience and Remote Sensing, Vol. 48 , No. 5, pp. 2297-2307, 2010.
[3]
Y. Liu; “Feature extraction and dimensionality reduction for mass spectrometry data”, Computers in Biology and Medicine, Vol. 39, pp. 818-823, 2009.
[4]
Y. Peng; Z. Wu; J. Jiang; “A novel feature selection approach for biomedical data classification”, J. Biomedical Informatics, Vol. 43, pp. 15-23, 2010.
[5]
F. Gorunesco; “Data Mining, Concepts, Models and Techniques”, Inteligent Systems Referencelibrary, Springer, Vol. 12, 2011.
[6]
Y. Saeys; I. Inza; P. Larranaga; “A review of feature selection techniques in Bioinformatics”, Bioinformatics, Vol. 23, pp. 2507-2517, 2007.
[7]
Z. Aghbari; Bayesian Network, <http://www.intechopen.com/books/bayesian-network/classification-of-categorical-and-numerical-data-on-selected-subset-of-features>, 2010.
[8]
Y. Yang; X. Guan; J. You; “CLOPE: A fast and effective clustering algorithm for transactional data”, Proceeding of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 682–687, 2002.
[9]
H. Qin; X. Ma; T. Herawan; J. Mohamad Zain; “MGR: An information theory based hierarchical divisive clustering algorithm for categorical data”, Knowledge-Based Systems, Vol. 67, pp. 401–411, 2014.
[10]
B. Pandey; R.B. Mishra; “Knowledge and intelligent computing system in medicine”, Computers in Biology and Medicine, Vol. 39, pp. 215-230, 2009.
[11]
T. W. S Chow; P. Wang; E. W. M. Ma; “A new feature selection scheme using a data distribution factor for unsupervised nominal data”, IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 38, pp. 499-509, 2008.
[12]
M. Blazadonakis; M. Zervakis; “Wrapper filtering criteria via linear neuron and kernel approaches”, Computers in Biology and Medicine, Vol. 38, pp. 894-912, 2008.
[13]
R. Blanco; P. Larranaga; I. Inza; B. Sierra; “Gene selection for cancer classification using wrapper approaches”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 18, pp. 1373-1390, 2004.
[14]
M. Pechenizkiy, A. Tsymbal; S. Puuronen; “Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis”, IEEE Transactions on Information Technology in Biomedicine, Vol. 10, pp. 533-539, 2006.
[15]
X. Q. Zeng; G. Z. Li; J. Y. Yang; “Dimension reduction with redundant gene elimination for tumor classification”, BMC Bioinformatics, Vol. 9, S-6 2008.
[16]
M. A. Hall; “Correlation-based feature selection for machine learning”, Ph.D. Thesis, Computer science department, University of Waikato at Hamilton, New Zealand, 1999.
[17]
H. Liu; L. Yu; “Toward integrated feature selection algorithms for classification and clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, pp. 491-502, 2005.
[18]
K. Kira; L. A.Rendell; “The Feature Selection Problem: Traditional methods and a new algorithm”, Proceedings of Ninth National Conference on Artificial Intelligence, pp. 129-134, 1992.
[19]
I. Kononenko; “Estimating attributes: Analysis and Extension of RELIEF”, Proceedings of European Conference on Machine Learning, pp. 171-182, 1994.
[20]
V. Kariwala; L. Ye; Y. Cao; “Branch and bound method for regression-based controlled variable selection”, Computers & Chemical Engineering, Vol. 54, pp. 1–7, 2013.
[21]
R.V. Rao; V.J. Savsani; D.P. Vakharia; “Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems”, Information Sciences, Vol. 183, Issue 1, pp. 1–15, 2012.
[22]
T. Mansouri; A. Farasat; M. B. Menhaj; M. Moghadam; “ARO: A new model free optimization algorithm for real time applications inspired by the asexual reproduction”, Expert Systems with Applications, Vol. 38, pp. 4866-4874, 2011.
[23]
F. J. Ferri; P. Pudil; M. Hatef; J. Kittler; “Comparative study of techniques for large scale feature selection”, Machine Intelligence and Pattern Recognition, Vol. 16, pp. 403-413, 1994.
[24]
E. Yilmaz; “An expert system based on fisher score and LS_SVM for cardiac Arrhythmia Diagnosis”, Computational and Mathematical Methods in Medicine, Vol. 2013, Article ID 849674, 6 pages, 2013.
[25]
M. Kudo; J. Sklansky; “Comparison of algorithms that select features for pattern Recognition”, Pattern Recognition, Vol. 33, pp. 25-41, 2000.
[26]
E.Namsrai; T. Munkhdalai; “A feature selection-based Ensemble method for arrhythmiaclassification”, Journal of Information Processing Systems, Vol. 9, pp. 31-40, 2013.
[27]
S. N. Ghazavi; T. W. Liao; “Medical data mining by fuzzy modeling with selected features”, Artificial Intelligence in Medicine, Vol. 43, pp. 195-206, 2008.
[28]
Y. H. Peng; “A novel ensemble machine learning for robust microarray data Classification”, Computers in Biology and Medicine, Vol. 36, pp. 553-573, 2006.
[29]
X. Zhang; X. Lu; “Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data”, BMC Bioinformatics, Vol. 7, 2006.
[30]
P. Somol; J. Novovicova J; P. Pudil; “Flexible-hybrid sequential floating search in statistical feature selection”, Lecture notes in computer science, Springer-Verlag, Vol. 41, pp. 632-639, 2006.
[31]
A. Asuncion; D. J. Newman; <http://www.ics.uci.edu/~mlearn/MLRepository.html> UCI machine learning repository, School of Information and Computer Science, University of California at Irvine, 2007.
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