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Reseach Article

Rough Sets and Rule Induction in Imperfect Information Systems

by Do Van Nguyen, Koichi Yamada, Muneyuki Unehara
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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, Muneyuki Unehara . Rough Sets and Rule Induction in Imperfect Information Systems. International Journal of Computer Applications. 89, 5 ( March 2014), 1-8. DOI=10.5120/15495-4286

@article{ 10.5120/15495-4286,
author = { Do Van Nguyen, Koichi Yamada, Muneyuki Unehara },
title = { Rough Sets and Rule Induction in Imperfect Information Systems },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 5 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number5/15495-4286/ },
doi = { 10.5120/15495-4286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:26.160966+05:30
%A Do Van Nguyen
%A Koichi Yamada
%A Muneyuki Unehara
%T Rough Sets and Rule Induction in Imperfect Information Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 5
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Imperfect Information Systems Probability of Matching Approximation Space Rough Sets Decision Rules