References

Artur, J.F. and A.T. Mario, 2012. Efficient variable selection filters for high-dimensional data. Pattern Recognit. Lett., 33: 1794-1804.

Bache, K. and M. Lichman, 2013. UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA., USA.

Balamurugan, S.A.A. and R. Rajaram, 2009. Effective and efficient feature selection for large-scale data using Bayes' theorem. Int. J. Autom. Comput., 6: 62-71.
CrossRef  |  Direct Link  |  

Bermejo, P., L. de la Ossa, J.A. Gamez and J.M. Puerta, 2012. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowledge-Based Syst., 25: 35-44.
CrossRef  |  Direct Link  |  

Cheng, W. and E. Hullermeier, 2009. Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn., 76: 211-225.
CrossRef  |  

Dash, M., H. Liu and H. Motoda, 2000. Consistency based variable selection. Proceedings of the 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, April 18-20, 2000, Kyoto, Japan, pp: 98-109.

Garcia, S., J. Luengo, J.A. Saez, V. Lopez and F. Herrera, 2013. A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning. IEEE Trans. Knowledge Data Eng., 25: 734-750.
CrossRef  |  

Gheyas, I.A. and L.S. Smith, 2010. Variable subset selection in large dimensionality domains. Pattern Recognit., 43: 5-13.
CrossRef  |  Direct Link  |  

Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I.H. Witten, 2009. The WEKA data mining software: An update. SIGKDD Explorat. Newslett, 11: 10-18.
CrossRef  |  Direct Link  |  

Hall, M.A., 1999. Correlation-based variable selection for machine learning. Ph.D. Thesis, The University of Waikato, New Zealand.

Handl, J. and J. Knowles, 2006. Feature subset selection in unsupervised learning via multiobjective optimization. Int. J. Comput. Intell. Res., 2: 217-238.
Direct Link  |  

Hou, C., F. Nie, X. Li, D. Yi and Y. Wu, 2013. Joint embedding learning and sparse regression: A framework for unsupervised feature selection. IEEE Trans. Cybernetics, Vol. 10. 10.1109/TCYB.2013.2272642

Hung, H.J. and C.N. Hsu, 2002. Bayesian classification for data from the same unknown class. IEEE Trans. Syst. Man Cybernetics B: Cybernetics, 32: 137-145.
CrossRef  |  Direct Link  |  

Lee, M.C., 2009. Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst. Appl., 36: 10896-10904.
CrossRef  |  Direct Link  |  

Liu, H. and L. Yu, 2005. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge Data Eng., 17: 491-502.
CrossRef  |  Direct Link  |  

Liu, H., J. Sun, L. Liu and H. Zhang, 2009. Feature selection with dynamic mutual information. Pattern Recogn., 42: 1330-1339.
CrossRef  |  

Mangai, J.A., V.S. Kumar and S.A.A. Balamurugan, 2012. A novel feature selection framework for automatic web page classification. Int. J. Automation Comput., 9: 442-448.
CrossRef  |  

Mitra, P., C.A. Murthy and S.K. Pal, 2002. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern. Anal. Mach. Intell., 24: 301-312.
CrossRef  |  Direct Link  |  

Pan, S.J. and Q. Yang, 2010. A survey on transfer learning. IEEE Trans. Knowledge Data Eng., 22: 1345-1359.
CrossRef  |  Direct Link  |  

Peng, H., L. Fulmi and C. Ding, 2005. Variable selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Machine Intell., 27: 1226-1238.

Peng, W., S. Cesar and S. Edward, 2013. Prediction based on integration of decisional DNA and a variable selection algorithm RELIEF-F. Cybernetics Syst., 44: 173-183.

Polat, K. and S. Gunes, 2009. A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst. Appl., 36: 1587-1592.
CrossRef  |  

Rahman, R.M. and F.R.M. Hasan, 2011. Using and comparing different decision tree classification techniques for mining ICDDR, B hospital surveillance data. Expert Syst. Appl., 38: 11421-11436.
CrossRef  |  Direct Link  |  

Robnik-Sikonja, M. and I. Kononenko, 2003. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learn., 53: 23-69.
CrossRef  |  Direct Link  |  

Ruggieri, S., 2002. Efficient C4. 5 [classification algorithm]. IEEE Trans. Knowledge Data Eng., 14: 438-444.
CrossRef  |  

Song, Q., J. Ni and G. Wang, 2013. A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowledge Data Eng., 25: 1-14.
CrossRef  |  

Srivastava, A., S. Ghosh, N. Anantharaman and V.K. Jayaraman, 2013. Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests. J. Immunol. Methods, 387: 284-292.
CrossRef  |  PubMed  |  

Sun, Y., S. Todorovic and S. Goodison, 2010. Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans. Pattern Anal. Machine Intell., 32: 1610-1626.
CrossRef  |  Direct Link  |  

Uguz, H., 2011. A two-stage variable selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Syst., 24: 1024-1032.
Direct Link  |  

Vinh, N.X. and J. Bailey, 2013. Comments on supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognit., 46: 1220-1225.
CrossRef  |  

Wei, H.L. and S.A. Billings, 2007. Feature subset selection and ranking for data dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell., 29: 162-166.
CrossRef  |  Direct Link  |  

Wu, J., Y. Feng, Z. Zheng, M.C. Zhou and Z. Wu, 2012. Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Trans. Syst. Man Cybernetics: Syst., 43: 428-439.
CrossRef  |  

Yu, L. and H. Liu, 2004. Efficient variable selection via analysis of relevance and redundancy. J. Mach. Learn. Res., 5: 1205-1224.
Direct Link  |