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Rough Sets and Rule Induction in Imperfect Information Systems

by Do Van Nguyen, Koichi Yamada, Muneyuki Unehara
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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

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