SOAP: Efficient feature selection of numeric attributes

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2527, Issue , 2002, Pages 233-242

  Ruiz, Roberto ^a   Aguilar Ruiz, Jesús S ^a   Riquelme, José C ^a  

^a UNIVERSITY OF SEVILLE   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTE REDUCTION; ATTRIBUTE SELECTION; BEFORE AND AFTER; CLASSIFICATION ALGORITHM; CLASSIFICATION MODELS; COMPUTATIONAL COSTS; DATA SETS; EFFICIENT FEATURE SELECTIONS; EVALUATION STRATEGIES; PREPROCESSING PHASE; RELIEFF; SELECTION OF ATTRIBUTES; STOPPING CRITERIA; STATISTICAL CALCULATIONS;

ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; METADATA;

ALGORITHMS;

EID: 34548108870     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36131-6_24     Document Type: Conference Paper

References (19)

1. Aguilar-Ruiz, Jesús S., Riquelme, José C. and Toro, Miguel. Data Set Editing by Ordered Projection. Intelligent Data Analysis Journal. Vol. 5, no5, pp. 1-13, IOS Press (2001).
2. Almuallim, H. and Dietterich, T.G. Learning with many irrelevant features. In Proceedings of the Ninth National Conference on Artificial Intelligence. pp. 547-552. AAAI Press (1991).
3. Almuallim, H. and Dietterich, T.G. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1-2):279-305 (1994).
4. Blake, C. and Merz, E. K. UCI Repository of machine learning databases (1998).
5. Brassard, G., and Bratley, P. Fundamentals of algorithms. Prentice Hall, New Jersey (1996).
6. Hall M.A. Correlation-based feature selection for machine learning. PhD thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand (1998).
7. Hoare, C. A. R. QuickSort. Computer Journal, 5(1):10-15 (1962).
8. Kira, K. and Rendell, L. A practical approach to feature selection. In Proceedings of the Ninth International Conference on Machine Learning. pp. 249-256, Morgan Kaufmann (1992).
9. Kohavi, R. and John, G. H. Wrappers for feature subset selection. Artificial Intelligence, 97, 273-324 (1997).
10. Koller, D. and Sahami, M. Toward optimal feature selection. In Proceedings of the Thirteenth International Conference on Marching Learning. pp. 284-292, Morgan Kaufmann (1996).
11. Kononenko, I. Estimating attibutes: Analisys and extensions of relief. In Proceedings of the Seventh European Conference on Machine Learning. pp. 171-182, Springer-Verlag (1994).
12. Modrzejewski, M. Feature selection using rough sets theory. In Proceedings of the European Conference on Machine Learning. pp. 213-226, Springer (1993).
13. Pagallo, G. and Haussler, D. Boolean feature discovery in empirical learning. Machine Learning, 5, 71-99 (1990).
14. Quinlan, J. Induction of decision trees. Machine Learning, 1(1), 81-106 (1986).
15. Quinlan, J. C4.5: Programs for machine learning. Morgan Kaufmann (1993).
16. Robnik-Šikonja, M. and Kononenko, I. An adaption of relief for attribute estimation in regression. In Proceedings of the Fourteenth International Conference on Machine Learning. pp. 296-304, Morgan Kaufmann (1997).
17. Setiono, R., and Liu, H. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence (1995).
18. Setiono, R., and Liu, H. A probabilistic approach to feature selection-a filter solution. In Proceedings of International Conference on Machine Learning, 319-327 (1996).
19. Setiono, R., and Liu, H. Neural network feature selectors. IEEE Trans. On Neural Networks, 8(3), 654-662 (1997).