Potential of artificial intelligence based feature selection methods in regression models

Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999

Volumn , Issue , 1999, Pages 159-163

  Pudil, P ^a   Fuka, K ^a   Beranek, K ^a   Dvorak, P ^a  

^a UNIVERSITY OF ECONOMICS   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; REGRESSION ANALYSIS;

CLASSICAL METHODS; FEASIBLE ALTERNATIVES; FEATURE SELECTION METHODS; FEATURE SELECTION PROBLEM; FLOATING SEARCHES; LEARNING APPROACH; MULTIPLE REGRESSIONS; STATISTICAL PATTERN RECOGNITION;

PATTERN RECOGNITION;

EID: 72349094331     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIMA.1999.798521     Document Type: Conference Paper

References (7)

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4. Kudo M., Sklansky J. A Comparative Evaluation ofMedium-and Large-Scale Feature Selectors for Pattern Classifier. Proceedings of the 1th International Workshop STIPR'97 on Statistical Techniques in Pattern Recognition, Prague, Czech Republic, June 9-11, 1997, 91-96, 1997.
5. Pudil, P., Novovicova, J., Ferri, F.: Methods ofDimensionality Reduction in Statistical Pattern Recognition. Proceedings of the IEE European Workshop CMP '94, Prague, Institute ofInformation Theory and Automation, pp. 185-189.
6. Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. In: Pattern Recognition Letters, 15, 1994, pp. 1119-1125.
7. Pudil, P., Fuka, K., Beranek, K., Dvorak, P.: Use of Floating Search Methods in Regression Models. Proceedings of the workshop MME '99 ( Mathematical Methods in Economics "), Jindfichuv Hradec, Czech Republic, September 1999 (inprint).