Feature selection using data envelopment analysis

Knowledge-Based Systems

Volumn 64, Issue , 2014, Pages 70-80

  Zhang, Yishi ^a   Yang, Anrong ^a   Xiong, Chan ^a   Wang, Teng ^b   Zhang, Zigang ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b AUBURN UNIVERSITY   (United States)

Author keywords

Data envelopment analysis; Feature selection; Redundancy; Relevance; Super efficiency

Indexed keywords

DATA ENVELOPMENT ANALYSIS; FEATURE EXTRACTION; REDUNDANCY;

CONDITIONAL INDEPENDENCES; EFFICIENCY EVALUATION; EVALUATION CRITERIA; FEATURE ACQUISITION; FEATURE SELECTION METHODS; RELEVANCE; SELECTION FRAMEWORK; SUPER-EFFICIENCY;

EFFICIENCY;

EID: 84901192462     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.03.022     Document Type: Article

References (55)

1. L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy J. Mach. Learn. Res. 5 2004 1205 1224
2. S. Cang, and H. Yu Mutual information based input feature selection for classification problems Decis. Support Syst. 54 2012 691 698
3. I. Guyon, and A. Elisseeff An introduction to variable and features election J. Mach. Learn. Res. 3 2003 1157 1182
4. H. Liu, and L. Yu Toward integrating features election algorithms for classification and clustering IEEE Trans. Knowl. Data Eng. 17 4 2005 491 502
5. R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers San Mateo, CA, USA
6. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 2002 389 422
7. 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 16 10 2000 906 914
8. G. Qu, S. Hariri, and M. Yousif A new dependency and correlation analysis for features IEEE Trans. Knowl. Data Eng. 17 9 2005 1199 1207
9. H. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 8 2005 1226 1238
10. D. Huang, and T.W.S. Chow Effective feature selection scheme using mutual information Neurocomputing 63 2005 325 343
11. J.J. Huang, Y.Z. Cai, and X.M. Xu A parameterless feature ranking algorithm based on mi Neurocomputing 71 2008 1656 1668
12. L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt Feature selection via dependence maximization J. Mach. Learn. Res. 13 2012 1393 1434
13. Z. Zhao, and H. Liu Spectral feature selection for supervised and unsupervised learning Proceedings of the 24th International Conference on Machine learning, ICML'07 2007 ACM Press New York, NY, USA 1151 1157
14. L. Song, A.J. Smola, K.M. Borgwardt, and J. Bedo Supervised feature selection via dependence estimation Proceedings of the 24th International Conference on Machine learning, ICML'07 2007 ACM Press New York, NY, USA 823 830
15. K. Kira, and L. Rendell A practical approach to feature selection Proceedings of the 9th International Workshop on Machine Learning, ML'92 1992 Morgan Kaufmann San Francisco, CA, USA 249 256
16. I. Kononenko Estimating attributes: analysis and extensions of relief Proceedings of European Conference on Machine Learning, ECML'94 1994 Springer-Verlag New York, Inc. Secaucus, NJ, USA 171 182
17. R. Battiti Using mi for selecting features in supervised neural net learning IEEE Trans. Neural Netw. 5 4 1994 537 550
18. M.A. Hall Correlation-based feature selection for discrete and numeric class machine learning Proceedings of the 7th International Conference on Machine Learning, ICML'00 2000 Morgan Kaufmann Los Altos, CA, USA 359 366
19. F. Fleuret Fast binary feature selection with conditional mutual information J. Mach. Learn. Res. 5 2004 1531 1555
20. I. Tsamardinos, C. Aliferis, and A. Statnikov Local causal and markov blanket induction for causal discovery and feature selection for classification Part I: Algorithms and empirical evaluation J. Mach. Learn. Res. 11 2010 171 234
21. X. Wu, K. Yu, H. Wang, and W. Ding Online streaming feature selection Proceedings of the 27th International Conference on Machine Learning, ICML'10 2010 Omnipress Madison, WI, USA 1159 1166
22. E. Amaldi, and V. Kann On the approximation of minimizing non zero variables or unsatisfied relations in linear systems Theor. Comput. Sci. 209 1-2 1998 237 260
23. A.A. Albrecht Stochastic local search for the feature set problem, with applications to microarray data Appl. Math. Comput. 183 2 2006 1148 1164
24. N. Kwak, and C.H. Choi Input feature selection for classification problems IEEE Trans. Neural Netw. 13 1 2002 143 159
25. G.H. John, R. Kohavi, and K. Pfleger Irrelevant features and the subset selection problem Proceedings of the 11th International Conference on Machine Learning, ICML'94 1994 Morgan Kaufmann Publishers San Francisco, CA 121 129
26. H.H. Yang, J. Moody, Feature selection based on joint mutual information, in: Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis, 1999, pp. 22-25.
27. D. Koller, and M. Sahami Toward optimal feature selection Proceedings of International Conference on Machine Learning, ICML'96 1996 Morgan Kaufmann Los Altos, CA, USA 284 292
28. R. Kohavi, and G.H. John Wrappers for feature subset selection Artif. Intell. 97 1-2 1997 273 324
29. C. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data Proceedings of the IEEE Computer Society Conference on Bioinformatics, CSB'03 2003 IEEE Computer Society Washington, DC, USA 523 528
30. G. Wang, F.H. Lochovsky, and Q. Yang Feature selection with conditional mutual information maximin in text categorization Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM'04 2004 ACM Press New York, NY, USA 342 349
31. Y. Zhang, S. Li, T. Wang, and Z. Zhang Divergence-based feature selection for separate classes Neurocomputing 101 2013 32 42
32. I. Tsamardinos, C.F. Aliferis, A. Statnikov, Algorithms for large scale markov blanket discovery, in: Proceedings of the 16th International Florida Artificial Intelligence Research Society Conference, FLAIRS'03, AAAI Press, Menlo Park, CA, USA, 2003, pp. 376-381.
33. Y. Zhang, and Z. Zhang Feature subset selection with cumulate conditional mutual information minimization Expert Syst. Appl. 39 5 2012 6078 6088
34. Q. Song, J. Ni, and G. Wang A fast clustering-based feature subset selection algorithm for high-dimensional data IEEE Trans. Knowl. Data Eng. 25 1 2013 1 14
35. J.M. Sotoca, and F. Pla Supervised feature selection by clustering using conditional mutual information-based distances Pattern Recogn. 43 6 2010 2068 2081
36. G. Brown, A. Pocock, M.-J. Zhao, and M. Luján Conditional likelihood maximisation: a unifying framework for information theoretic feature selection J. Mach. Learn. Res. 13 1 2012 27 66
37. N.X. Vinha, and J. Bailey Comments on supervised feature selection by clustering using conditional mutual information-based distances Pattern Recogn. 46 4 2013 1220 1225
38. S. Yaramakala, and D. Margaritis Speculative markov blanket discovery for optimal feature selection Proceedings of the 5th IEEE International Conference on Data Mining, ICDM'05 2005 IEEE Computer Society Press Washington, DC, USA 809 812
39. I. Tsamardinos, L.E. Brown, and C.F. Aliferis The max-min hill climbing bayesian network structure learning algorithm Mach. Learn. 65 1 2006 31 78
40. J.M. Peòa, R. Nilsson, J. Björkegren, and J. Tegnér Towards scalable and data efficient learning of markov boundaries Int. J. Approx. Reason. 45 2 2007 211 232
41. A. Charnes, W.W. Cooper, and E. Rhodes Measuring the efficiency of decision making units Eur. J. Oper. Res. 2 1978 429 444
42. P. Andersen, and N. Petersen A procedure for ranking efficient units in data envelopment analysis Manage. Sci. 39 1993 1261 1264
43. W.D. Cook, and L.M. Seiford Data envelopment analysis (DEA) - thirty years on Eur. J. Oper. Res. 192 1 2009 1 17
44. N. Karmarkar A new polynomial time algorithm for linear programming Combinatorica 4 4 1984 373 395
45. M. Robnik-Sikonja, and I. Kononenko Theoretical and empirical analysis of relief and relief Mach. Learn. 53 2003 23 69
46. H.I. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2000 Morgan Kaufmann San Francisco, CA, USA
47. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous valued attributes for classification learning, in: Proceedings of the 13th International Joint Conference on Artificial Intelligence, IJCAI'93, 1993, pp. 1022-1027.
48. D. Aha, and D. Kibler Instance-based learning algorithms Mach. Learn. 6 1991 37 66
49. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000 Cambridge University Press Cambridge, UK
50. A. Azadeha, M. Saberi, R.T. Moghaddam, and L. Javanmardi An integrated data envelopment analysis-artificial neural network-rough set algorithm for assessment of personnel efficiency Expert Syst. Appl. 38 3 2011 1364 1373
51. T.R. Sexton, R.H. Silkman, A.J. Hogan, Data envelopment analysis: critique and extensions, in: R.H. Silkman (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis, Jossey-Bass, San Francisco, CA, 1986, pp. 73-105.
52. W.W. Cooper, L.M. Seiford, and K. Tone Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-Solver Software 2007 Springer New York, USA
53. J.T. Pastor, J.L. Ruiz, and I. Sirvent An enhanced DEA Russell graph efficiency measure Eur. J. Oper. Res. 115 3 1999 596 607
54. W.W. Cooper, K.S. Park, and J.T. Pastor Ram: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measure in DEA J. Prod. Anal. 11 1999 5 42
55. W.W. Cooper, J.T. Pastor, F. Borras, J. Aparicio, and D. Pastor Bam: a bounded adjusted measure of efficiency for use with bounded additive models J. Prod. Anal. 35 2 2011 85 94