A new algorithm for feature selection based on rough sets theory
DOI:
https://doi.org/10.17533/udea.redin.19021Keywords:
Feature selection, reduct, rough setsAbstract
Rough Sets Theory has opened new trends for the development of data analysis techniques. In this theory, the notion of reduct is very significant, but obtaining a reduct in a decision system is an expensive computing process although very important in data analysis and new discoveries. Because of this, it has been necessary to develop different variants to calculate reducts. The present work looks into the utility that offers Rough Sets in feature selection and a new method is presented with the purpose of calculate a good reduct. This new method consists of a greedy algorithm that uses heuristics to work out a good reduct in acceptable times. Experimental results obtained by using different data sets are presented.
Downloads
References
N. Zhong, J. Dong, S. Ohsuga “Using Rough sets with heuristics for feature selection” Journal of Intelligent Information Systems. Vol. 16. 2001. pp. 199-214. DOI: https://doi.org/10.1023/A:1011219601502
J. Wroblewski. “Finding minimal reducts using genetic algorithms”. En: Wang, P.P. (Ed). Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences, North Carolina, USA. 1995. pp. 186-189.
J.S Deogun. “Feature selection and effective classifiers”. Journal of ASIS. Vol 49. 1998. pp. 423-434. DOI: https://doi.org/10.1002/(SICI)1097-4571(19980415)49:5<423::AID-ASI5>3.3.CO;2-S
S.K. Choubey. “A comparison of feature selection algorithms in the context of rough classifiers”. Proceedings of Fifth IEEE International Conference on Fuzzy Systems. Vol. 2. 1996. pp. 1122-1128.
R. Kohavi, B. Frasca. “Useful feature subsets and Rough set Reducts”. Proceedings of the Third International Workshop on Rough Sets and Soft Computing. San José, California. 1994. pp. 310-317.
H. Liu, H. Motoda. “Feature Selection Boston, MA : Kluwer academic Publishers”. En: http://citeseer.ist.psu.edu/321378.html 1998. Consultado el 7 de mayo de 2006.
R. Jensen, S. Qiang. “Finding rough sets reducts with Ant colony optimization”. http://www.inf.ed.ac.uk/publications/online/0201.pdf. 2003. Consultado el 5 de noviembre de 2005. DOI: https://doi.org/10.1016/j.fss.2004.07.014
Z. Pawlak. “Rough sets”. International Journal of Information & Computer Sciences. Vol. 11. 1982. pp. 341-356. DOI: https://doi.org/10.1007/BF01001956
J. Komorowski, Z. Pawlak, Z. “Rough Sets: A tutorial. In Pal, S.K. and Skowron, A. (Eds.) Rough Fuzzy Hy-bridization: A new trend in decision-making.” Springer. 1999. pp. 3-98.
I. Dunstsh, Ivo, G. Gunter. “Rough set data analysis”. En: http://citeseer.nj.nec.com/dntsch00rough.html. 2000. Consultado el 20 de mayo de 2006.
Z. Pawlak, “Rough Sets Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht”. 1991. En: http://citeseer.ist.psu.edu/con-text/36378.html. Consultado el 7 de abril de 2006.
R. Bello, Z. Valdivia, M.M. García, L. Reynoso. Aplicaciones de la inteligencia artificial. México. 1ª. ed. Guadalajara. Universidad de Guadalajara. 2002. pp. 62-64
R. Bello, A. Nowe, A. Puris. “Two Step Ant Colony Sys-tem to Solve the Feature Selection Problem”. Lectures Notes on Computer Sciences. Springer-Verlag. 2006. pp. 588-596. DOI: https://doi.org/10.1007/11892755_61
B.S. Ahn. “The integrated methodology of rough set theory and artificial neural networks for business failure predictions”. Expert Systems with Applications. Vol. 18. 2000. pp. 65-74. DOI: https://doi.org/10.1016/S0957-4174(99)00053-6
A. Ohrn. “Rosetta Technical Reference Manual. Department of Computer and Information Science” Norwegian University of Science and Technology. Noruega, 2002.
Y. S. Wong, C. J. Butz. “Methodologies for Knowledge Discovery and Data Mining”. Zhong y Zhou (Eds.) On Information-Theoretic Measures of attribute importance. pp. 231-238. En: http://citeseer.ist.psu. edu/yao99informationtheoretic.html. Consultado el 15 de febrero de 2006.
P. Piñero, L. Arco, M.M. García, Y. Caballero.“Two New Metrics for Feature Selection in Pattern Recognition”, Lectures Notes in Computer Science (LNCS 2905) Springer Verlag, Berlin Heidelberg. 2003. pp. 488-497 DOI: https://doi.org/10.1007/978-3-540-24586-5_60
T. Mitchell, M. Hill. “Machine Learning”. 1997. http://www.cs.cmu.edu/~tom/mlbook.html. Consultado el 14 de enero de 2006.
C. L Blake, C. J. Merz. “UCI Repository of machine learning databases”. University of California, Irvine, 1998 http://www.ics.uci.edu/~mlearn/. Consultado el 10 de mayo de 2005.
D. Álvarez. Feature selection for data analysis using Rough Sets Theory. Master Thesis of Computer Science Engineering. University of Camagüey. Cuba. 2005. pp. 38-66.
Downloads
Published
How to Cite
Issue
Section
License
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.
Twitter