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Fuzzy-rough instance selection

Richard Jensen and Chris Cornelis UGent (2010) IEEE International Conference on Fuzzy Systems. p.1776-1782
abstract
Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully utilised to devise mathematically sound and often, computationally efficient techniques for addressing problems such as hidden pattern discovery from data, feature selection and decision rule generation. Fuzzy-rough set theory improves upon this by enabling uncertainty and vagueness to be modeled more effectively. Recently, the value of fuzzy-rough sets for feature selection and rule induction has been established. However, the potential of this theory for instance selection has not been investigated at all. This paper proposes three novel methods for instance selection based on fuzzy-rough sets. The initial experimentation demonstrates that the methods can significantly reduce the number of instances whilst maintaining high classification accuracies.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
in
IEEE International Conference on Fuzzy Systems
issue title
2010 IEEE international conference on fuzzy systems (FUZZ-IEEE 2010)
pages
1776 - 1782
publisher
IEEE
place of publication
New York, NY, USA
conference name
2010 IEEE World congress on Computational Intelligence (WCCI 2010)
conference location
Barcelona, Spain
conference start
2010-07-18
conference end
2010-07-23
Web of Science type
Proceedings Paper
Web of Science id
000287453602137
ISSN
1098-7584
ISBN
9781424469208
9781424481262
DOI
10.1109/FUZZY.2010.5584791
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1224877
handle
http://hdl.handle.net/1854/LU-1224877
date created
2011-05-16 17:09:53
date last changed
2018-05-17 14:37:16
@inproceedings{1224877,
  abstract     = {Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully utilised to devise mathematically sound and often, computationally efficient techniques for addressing problems such as hidden pattern discovery from data, feature selection and decision rule generation. Fuzzy-rough set theory improves upon this by enabling uncertainty and vagueness to be modeled more effectively. Recently, the value of fuzzy-rough sets for feature selection and rule induction has been established. However, the potential of this theory for instance selection has not been investigated at all. This paper proposes three novel methods for instance selection based on fuzzy-rough sets. The initial experimentation demonstrates that the methods can significantly reduce the number of instances whilst maintaining high classification accuracies.},
  author       = {Jensen, Richard and Cornelis, Chris},
  booktitle    = {IEEE International Conference on Fuzzy Systems},
  isbn         = {9781424469208},
  issn         = {1098-7584},
  language     = {eng},
  location     = {Barcelona, Spain},
  pages        = {1776--1782},
  publisher    = {IEEE},
  title        = {Fuzzy-rough instance selection},
  url          = {http://dx.doi.org/10.1109/FUZZY.2010.5584791},
  year         = {2010},
}

Chicago
Jensen, Richard, and Chris Cornelis. 2010. “Fuzzy-rough Instance Selection.” In IEEE International Conference on Fuzzy Systems, 1776–1782. New York, NY, USA: IEEE.
APA
Jensen, Richard, & Cornelis, C. (2010). Fuzzy-rough instance selection. IEEE International Conference on Fuzzy Systems (pp. 1776–1782). Presented at the 2010 IEEE World congress on Computational Intelligence (WCCI 2010), New York, NY, USA: IEEE.
Vancouver
1.
Jensen R, Cornelis C. Fuzzy-rough instance selection. IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE; 2010. p. 1776–82.
MLA
Jensen, Richard, and Chris Cornelis. “Fuzzy-rough Instance Selection.” IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE, 2010. 1776–1782. Print.