Abstract
The attribute selection problem with respect to decision tables can be efficiently solved with the use of rough set theory. However, a known issue in standard rough set methodology is its inability to deal with probabilistic and similarity information about objects. This paper presents a novel type of reduct that takes into account this information. We argue that the approximate preservation of probability distributions and similarity of objects within reduced decision table helps to preserve the quality of its classification capability.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Kryszkiewicz, M.: Comparative study of alternative types of knowledge reduction in inconsistent systems. Int. J. Intell. Syst. 16(1), 105–120 (2001)
Pawlak, Z.: Information systems – theoretical foundations. Information Systems 6, 205–218 (1981)
Shen, Q., Jensen, R.: Rough sets, their extensions and applications. International Journal of Automation and Computing 4, 217–228 (2007)
Slezak, D., Ziarko, W.: Attribute reduction in the bayesian version of variable precision rough set model. Electr. Notes Theor. Comput. Sci. 82(4) (2003)
Stefanowski, J., Tsoukiàs, A.: Induction of decision rules and classification in the valued tolerance approach. In: Rough Sets and Current Trends in Computing, pp. 271–278 (2002)
Zhang, W., Mi, J., Wu, W.: Approaches to knowledge reductions in inconsistent information systems. International Journal of Intelligent Systems (2003)
Ziarko, W.: Stochastic approach to rough set theory. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 38–48. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Froelich, W., Wakulicz-Deja, A. (2011). Probabilistic Similarity-Based Reduct. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_77
Download citation
DOI: https://doi.org/10.1007/978-3-642-24425-4_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
eBook Packages: Computer ScienceComputer Science (R0)