Empirical study of feature selection methods based on individual feature evaluation for classification problems

Expert Systems with Applications

Volumn 38, Issue 7, 2011, Pages 8170-8177

  Arauzo Azofra, Antonio ^a   Aznarte, José Luis ^b   Benítez, José M ^c  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b PSL RESEARCH UNIVERSITY   (France)

^c UNIVERSITY OF GRANADA   (Spain)

Author keywords

Classification problems; Data reduction; Feature estimation; Feature evaluation; Feature selection

Indexed keywords

AUTOMATIC FEATURE SELECTION; CLASSIFICATION PROBLEMS; EMPIRICAL STUDIES; FEATURE ESTIMATION; FEATURE EVALUATION; FEATURE SELECTION; FEATURE SELECTION METHODS; LEARNING PROCESS; MODULARIZATIONS; RUNNING TIME; SELECTION CRITERIA; UNDERSTANDABILITY; WIDE SPECTRUM;

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; MODULAR CONSTRUCTION; STATISTICS;

FEATURE EXTRACTION;

EID: 79952437887     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.12.160     Document Type: Article

References (33)

1. N. Amjady, and A. Daraeepour Design of input vector for day-ahead price forecasting of electricity markets Expert Systems with Applications 36 10 2009 12281 12294
2. Arauzo-Azofra, A. (2006). Orange snns module. Available from: .
3. A. Arauzo-Azofra, J.M. Benitez, and J.L. Castro Consistency measures for feature selection Journal of Intelligent Information Systems 30 3 2008 273 292 (Pubitemid 351667132)
4. L. Breiman, Classification and regression trees 1998 Chapman & Hall
5. T.M. Cover, and J.A. Thomas Elements of information theory 1991 Wiley Interscience
6. M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 1-4 1997 131 156
7. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
8. Demsar, J., Zupan, B., (2004). Orange: From experimental machine learning to interactive data mining, (White paper). Available from: .
9. Demsar, J., Zupan, B., Leban, G. (2006). Datasets. Available from: .
10. R.O. Duda, P.E. Hart, and D.G. Stork Pattern classification 2nd ed. 2001 Wiley Interscience
11. Fix, E., Hodges, J. (1951). Discriminatory analysis, nonparametric discrimination: Consistency properties. Tech. Rep. 4, USAF School of Aviation Medicine, Randolph Field, Texas.
12. Graphics, S. (2006). Datasets. Available from: .
13. T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning: Data mining, inference, and prediction 2001 Springer-Verlag New York
14. S. Haykin Neural networks, a comprehensive foundation 1999 Prentice-Hall
15. Hettich, S., Bay, S. D. (2008). Uci machine learning repository, .
16. K. Hornik, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Networks 2 1989 359 366 (Pubitemid 20609008)
17. A. Jain, and D. Zongker Feature selection: Evaluation, application and small sample performance IEEE Transactions Pattern Analysis and Machine Intelligence 19 2 1997 153 158 (Pubitemid 127828334)
18. S.G. Kemal Polat A new feature selection method on classification of medical datasets: Kernel f-score feature selection Expert Systems with Applications 36 7 2009 10367 10373
19. K. Kira, and L.A. Rendell A practical approach to feature selection Proceedings of the ninth international conference on machine learning 1992 Morgan Kaufmann Aberdeen, Scotland 249 256
20. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324 (Pubitemid 127401107)
21. Kononenko, I., (1994). Estimating attributes: Analysis and extensions of RELIEF. In European Conference on Machine Learning (pp. 171-182). Available from: .
22. P. Langley Selection of relevant features in machine learning Proceedings of the AAAI fall symposium on relevance 1994 AAAI Press New Orleans, LA, USA 1 5
23. Liu, H., Yu, L., (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(3).
24. H. Liu, F. Hussain, C.L. Tan, and M. Dash Discretization: An enabling technique Data Mining and Knowledge Discovery 6 2002 393 423 (Pubitemid 37113864)
25. H. Liu, and H. Motoda Feature selection for knowledge discovery and data mining 1998 Springer
26. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufman
27. Rish, I. (2001). An empirical study of the naive bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence.
28. M. Robnik-Sikonja, and I. Kononenko Theoretical and empirical analysis of relieff and rrelieff Machine Learning 53 2003 23 69
29. K. Thangavel, and A. Pethalakshmi Dimensionality reduction based on rough set theory: A review Applied Soft Computing 9 1 2008 1 12
30. U. of Stuttgart. (1995). Stuttgart neural network simulator. Available from: .
31. U. of Toronto. (2003). Data for evaluating learning in valid experiments. Available from: .
32. J.H. Zar Biostatistical analysis 4th ed. 1999 Prentice-Hall New Jersey (US)
33. Zhang, H. (2004). The optimality of naive Bayes. In FLAIRS conference.