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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)
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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.
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INSTANCE

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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.
@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},
}

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