Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

Expert Systems with Applications

Volumn 39, Issue 12, 2012, Pages 11094-11102

  Ruiz, R ^a   Riquelme, J C ^b   Aguilar Ruiz, J S ^a   Garcia Torres M ^a  

^a UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

^b UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Classification; Data mining; Feature ranking; Feature selection

Indexed keywords

CLASSIFICATION ERRORS; CLASSIFICATION TASKS; FEATURE RANKING; FEATURE SUBSET SELECTION; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; HYBRID APPROACH; HYBRID STRATEGIES; SEQUENTIAL FORWARD SELECTION; WRAPPER MODEL;

CLASSIFICATION (OF INFORMATION); DATA MINING; INFORMATION SYSTEMS; MATHEMATICAL MODELS;

FEATURE EXTRACTION;

EID: 84861186437     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.03.061     Document Type: Article

References (40)

1. A. Alizadeh, M. Eisen, R. Davis, C. Ma, I. Lossos, and A. Rosenwald Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 403 2000 503 511
2. U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, and D. Mack Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays Proceedings of the National Academy of Sciences USA 96 1999 6745 6750
3. A. Arauzo-Azofra, J.L. Aznarte, and J.M. Benítez Empirical study of feature selection methods based on individual feature evaluation for classification problems Expert Systems with Applications 38 7 2010 8170 8177
4. R. Armañanzas, Y. Saeys, I. Inza, M. García-Torres, C. Bielza, and Y. van de Peer Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 3 2011 760 774
5. N. Azam, and J. Yao Comparison of term frequency and document frequency based feature selection metrics in text categorization Expert Systems with Applications 39 5 2011 4760 4768
6. D. Bell, and H. Wang A formalism for relevance and its application in feature subset selection Machine Learning 41 2 2000 175 195
7. Bermejo, P.; Gámez, J.; Puerta, J.; (2008). On incremental wrapper-based attribute selection: Experimental analysis of the relevance criteria. In IPMU'08: proceedings of the 12th international conference on information processing and management of uncertainty in knowledge-based systems.
8. A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1-2 1997 245 271
9. Caruana, R.; Freitag, D. (1994). How useful is relevance? In Working notes of the AAAI fall symposium on relevance (pp. 25-29).
10. J. Chen, H. Huang, S. Tian, and Y. Qu Feature selection for text classification with Naïve Bayes Expert Systems with Applications 36 3 2008 5432 5435
11. S. Das Filters, wrappers and a boosting-based hybrid for feature selection 18th international conference on machine learning 2001 Morgan Kaufman Publishers Inc. 74 81
12. Dash, M.; Liu, H.; Motoda, H. (2000). Consistency based feature selection. In Pacific-Asia conference on knowledge discovery and data mining (pp. 98-109).
13. M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 3 1997 131 156
14. M. Dash, and H. Liu Consistency-based search in feature selection Artificial Intelligence 151 1-2 2003 155 176 http://dx.doi.org/10.1016/S0004- 370(03)00079-1
15. C. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data IEEE Computer Society Bioinformatics 2003 523 529
16. S. Foithong, O. Pinngern, and B. Attachoo Feature subset selection wrapper based on mutual information and rough sets Expert Systems with Applications 39 1 2011 574 584
17. Frank, A.; Asuncion, A. (2010). UCI machine learning repository. URL .
18. S. García, and F. Herrera An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons Journal of Machine Learning Research 9 2008 2677 2694
19. F.C. García-López, M. García-Torres, B. Melián-Batista, J.A. Moreno-Pérez, and J.M. Moreno-Vega Solving the feature selection problem by a parallel scatter search European Journal of Operations Research 169 2 2006 477 489
20. García-Torres, V.; García-López, F. C.; Melián-Batista, B.; Moreno-Pérez, J. A.; Moreno-Vega, J. M. (2004). Solving feature subset selection problem by a hybrid metaheuristic, In First international workshop in hybrid metaheuristics at ECAI 2004 (HM 2004) (pp. 59-69).
21. García-Torres, M.; Armañanzas, R.; Bielza, C.; Larrañaga, P.; (in press). Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data, .
22. T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, and J. Mesirov Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring Science 286 1999 531 537
23. I. Guyon, and A. Elisseeff An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
24. I. Guyon, S. Gunn, A. Ben-Hur, and G. Dror Result analysis of the nips 2003 feature selection challenge Advances in neural information processing systems 2005 MIT Press 545 552
25. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machine Machine Learning 46 1-3 2002 389 422
26. M. Hall Correlation-based feature selection for discrete and numeric class machine learning 17th international conference on machine learning 2000 Morgan Kaufmann San Francisco, CA 359 366
27. M. Hall, and G. Holmes Benchmarking attribute selection techniques for discrete class data mining IEEE Transactions on Knowledge and Data Engineering 15 3 2003
28. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The weka data mining software: An update SIGKDD Explorations Newsletters 11 2009 10 18 http://doi.acm.org/10.1145/1656274.1656278
29. M.M. Kabir, M. Shahjahan, and K. Murase A new hybrid ant colony optimization algorithm for feature selection Expert Systems with Applications 39 3 2011 3747 3763
30. R. Kohavi, and G. John Wrappers for feature subset selection Artificial Intelligence 1-2 1997 273 324
31. Koller, D.; Sahami, M. (1996). Toward optimal feature selection. In 13th International Conference on Machine Learning (pp. 284-292).
32. H. Liu, and L. Yu Toward integrating feature selection algorithms for classification and clustering IEEE Transactions on Knowledge and Data Engineering 17 3 2005 1 12
33. J. Pacheco, S. Casado, and L. Núnez Use of VNS and TS in classification: Variable selection and determination of the linear discrimination function coefficients IMA Journal of Management Mathematics 18 2 2007 191 206
34. J.A. Pérez-Benítez, and L.R. Padovese Feature selection and neural network for analysis of microstructural changes in magnetic materials Expert Systems with Applications 38 8 2011 10547 10553
35. W. Press, B. Flannery, S. Teukolsky, and W. Vetterling Numerical Recipes in C 1988 Press Syndicate of the University of Cambridge Cambridge
36. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C. Yeang, and M. Angelo Multiclass cancer diagnosis using tumor gene expression signatures Journal of computational Biology 7 3-4 2000 559 584
37. Saeys, Y.; Abeel, T.; de Peer, Y. V. (2008). Robust feature selection using ensemble feature selection techniques. In ECML/PKDD (2) (pp. 313-325).
38. E. Xing, M. Jordan, and R. Karp Feature selection for high-dimensional genomic microarray data Proceedings 18th international conference on machine learning 2001 Morgan Kaufman San Francisco, CA 601 608
39. Y. Yang, Y. Liao, G. Meng, and J. Lee A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis Expert Systems with Applications 38 9 2011 11311 11320
40. L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy Journal of machine learning research 5 2004 1205 1224