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Divergence Based Generalized Fuzzy Rough Sets

Sheeja T.K.1 , Sunny Kuriakose A.2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 809-815, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.809815

Online published on Jun 30, 2018

Copyright © Sheeja T.K., Sunny Kuriakose A. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sheeja T.K., Sunny Kuriakose A., “Divergence Based Generalized Fuzzy Rough Sets,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.809-815, 2018.

MLA Style Citation: Sheeja T.K., Sunny Kuriakose A. "Divergence Based Generalized Fuzzy Rough Sets." International Journal of Computer Sciences and Engineering 6.6 (2018): 809-815.

APA Style Citation: Sheeja T.K., Sunny Kuriakose A., (2018). Divergence Based Generalized Fuzzy Rough Sets. International Journal of Computer Sciences and Engineering, 6(6), 809-815.

BibTex Style Citation:
@article{T.K._2018,
author = {Sheeja T.K., Sunny Kuriakose A.},
title = {Divergence Based Generalized Fuzzy Rough Sets},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {809-815},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2260},
doi = {https://doi.org/10.26438/ijcse/v6i6.809815}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.809815}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2260
TI - Divergence Based Generalized Fuzzy Rough Sets
T2 - International Journal of Computer Sciences and Engineering
AU - Sheeja T.K., Sunny Kuriakose A.
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 809-815
IS - 6
VL - 6
SN - 2347-2693
ER -

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Abstract

Fuzzy set theory and rough set theory are two formal mathematical tools to handle vagueness, imperfection or incompleteness in data. Fuzzy rough set theory is an embodiment of the prime features of both the theories. This hybrid theory has been proved to be an effective tool for data mining, particularly for feature selection. In this paper, generalized fuzzy rough approximations based on divergence measure of fuzzy sets in an information system is defined using a fuzzy implicator and a fuzzy t-norm. Also, the properties of the fuzzy rough approximations are investigated. Further, an algorithm for feature selection using the fuzzy boundary region of the proposed approximations is presented and experimented with twelve real data sets.

Key-Words / Index Term

Information System, Approximations, Divergence Measure, Fuzzy rough set, Feature selection

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