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Rough Set Approach in Machine Learning: A Review

by Prerna Mahajan, Rekha Kandwal, Ritu Vijay
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 10
Year of Publication: 2012
Authors: Prerna Mahajan, Rekha Kandwal, Ritu Vijay
10.5120/8924-2996

Prerna Mahajan, Rekha Kandwal, Ritu Vijay . Rough Set Approach in Machine Learning: A Review. International Journal of Computer Applications. 56, 10 ( October 2012), 1-13. DOI=10.5120/8924-2996

@article{ 10.5120/8924-2996,
author = { Prerna Mahajan, Rekha Kandwal, Ritu Vijay },
title = { Rough Set Approach in Machine Learning: A Review },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number10/8924-2996/ },
doi = { 10.5120/8924-2996 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:27.017979+05:30
%A Prerna Mahajan
%A Rekha Kandwal
%A Ritu Vijay
%T Rough Set Approach in Machine Learning: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 10
%P 1-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Rule Induction Feature Selection

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