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Rough Sets and Rule Induction in Imperfect Information Systems

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 89 - Number 5
Year of Publication: 2014
Authors:
Do Van Nguyen
Koichi Yamada
Muneyuki Unehara
10.5120/15495-4286

Do Van Nguyen, Koichi Yamada and Muneyuki Unehara. Article: Rough Sets and Rule Induction in Imperfect Information Systems. International Journal of Computer Applications 89(5):1-8, March 2014. Full text available. BibTeX

@article{key:article,
	author = {Do Van Nguyen and Koichi Yamada and Muneyuki Unehara},
	title = {Article: Rough Sets and Rule Induction in Imperfect Information Systems},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {89},
	number = {5},
	pages = {1-8},
	month = {March},
	note = {Full text available}
}

Abstract

The original rough set theory deals with precise and complete data, while real applications frequently contain imperfect information. A typical imperfect data studied in rough set research is the missing values. Though there are many ideas proposed to solve the issue in the literature, the paper adopts a probabilistic approach, because it can incorporate other types of imperfect data including imprecise and uncertain values in a single approach. The paper first discusses probabilities of attribute values assuming different type of attributes in real applications, and proposes a generalized method of probability of matching. This probability is then used to define valued tolerance/similarity relations and to develop new rough set models based on the valued tolerance/similarity relations. An algorithm for deriving decision rules based on the rough set models is also studied and proposed.

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