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Fuzzy rough set prototype selection for regression

Sarah Vluymans UGent, Yvan Saeys UGent, Chris Cornelis UGent, Ankur Teredesai and Martine De Cock UGent (2015) IEEE International Fuzzy Systems Conference Proceedings.
abstract
Instance selection methods are a class of preprocessing techniques that have been widely studied in machine learning to remove redundant or noisy instances from a training set. The main focus of such prior efforts has been on the selection of suitable training instances to perform a classification task for crisp class labels. In this paper, we propose a novel instance selection technique termed Fuzzy Rough Set Prototype Selection for Regression (FRPS-R) for solving regression problems, where the outcome is continuous. We use concepts from fuzzy rough set theory and extend the currently well-known fuzzy rough set prototype selection technique to model the quality of all available elements and then use a wrapper approach to select an optimal subset of high-quality instances; thereby generalizing the idea. Our experimental evaluation shows that the application of our proposed instance selection technique can significantly improve the predictive performance of the weighted k-nearest neighbor regression algorithm, in particular when noise is present in the original training set.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
INSTANCE
in
IEEE International Fuzzy Systems Conference Proceedings
issue title
2015 IEEE International conference on fuzzy systems SYSTEMS (FUZZ-IEEE 2015)
pages
8 pages
publisher
IEEE
place of publication
New York, NY, USA
conference name
2015 IEEE International conference on Fuzzy Systems (FUZZ-IEEE)
conference location
Istanbul, Turkey
conference start
2015-08-02
conference end
2015-08-05
Web of Science type
Proceedings Paper
Web of Science id
000370288300127
ISSN
1544-5615
ISBN
9781467374286
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
6936278
handle
http://hdl.handle.net/1854/LU-6936278
date created
2015-09-23 13:30:55
date last changed
2018-05-18 06:43:12
@inproceedings{6936278,
  abstract     = {Instance selection methods are a class of preprocessing techniques that have been widely studied in machine learning to remove redundant or noisy instances from a training set. The main focus of such prior efforts has been on the selection of suitable training instances to perform a classification task for crisp class labels. In this paper, we propose a novel instance selection technique termed Fuzzy Rough Set Prototype Selection for Regression (FRPS-R) for solving regression problems, where the outcome is continuous. We use concepts from fuzzy rough set theory and extend the currently well-known fuzzy rough set prototype selection technique to model the quality of all available elements and then use a wrapper approach to select an optimal subset of high-quality instances; thereby generalizing the idea. Our experimental evaluation shows that the application of our proposed instance selection technique can significantly improve the predictive performance of the weighted k-nearest neighbor regression algorithm, in particular when noise is present in the original training set.},
  author       = {Vluymans, Sarah and Saeys, Yvan and Cornelis, Chris and Teredesai, Ankur and De Cock, Martine},
  booktitle    = {IEEE International Fuzzy Systems Conference Proceedings},
  isbn         = {9781467374286},
  issn         = {1544-5615},
  keyword      = {INSTANCE},
  language     = {eng},
  location     = {Istanbul, Turkey},
  pages        = {8},
  publisher    = {IEEE},
  title        = {Fuzzy rough set prototype selection for regression},
  year         = {2015},
}

Chicago
Vluymans, Sarah, Yvan Saeys, Chris Cornelis, Ankur Teredesai, and Martine De Cock. 2015. “Fuzzy Rough Set Prototype Selection for Regression.” In IEEE International Fuzzy Systems Conference Proceedings. New York, NY, USA: IEEE.
APA
Vluymans, S., Saeys, Y., Cornelis, C., Teredesai, A., & De Cock, M. (2015). Fuzzy rough set prototype selection for regression. IEEE International Fuzzy Systems Conference Proceedings. Presented at the 2015 IEEE International conference on Fuzzy Systems (FUZZ-IEEE), New York, NY, USA: IEEE.
Vancouver
1.
Vluymans S, Saeys Y, Cornelis C, Teredesai A, De Cock M. Fuzzy rough set prototype selection for regression. IEEE International Fuzzy Systems Conference Proceedings. New York, NY, USA: IEEE; 2015.
MLA
Vluymans, Sarah, Yvan Saeys, Chris Cornelis, et al. “Fuzzy Rough Set Prototype Selection for Regression.” IEEE International Fuzzy Systems Conference Proceedings. New York, NY, USA: IEEE, 2015. Print.