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Semi-supervised fuzzy-rough feature selection

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Abstract
With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90% of the data object labels are missing.
Keywords
Feature selection, Fuzzy-rough sets, Semi-supervised learning

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Please use this url to cite or link to this publication:

Chicago
Jensen, Richard, Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, and Yvan Saeys. 2015. “Semi-supervised Fuzzy-rough Feature Selection.” In Lecture Notes in Artificial Intelligence, ed. Yiyu Yao, Qinghua Hu, Hong Yu, and Jerzy W Grzymala-Busse, 9437:185–195. Cham, Switzerland: Springer.
APA
Jensen, Richard, Vluymans, S., Mac Parthaláin, N., Cornelis, C., & Saeys, Y. (2015). Semi-supervised fuzzy-rough feature selection. In Yiyu Yao, Q. Hu, H. Yu, & J. W. Grzymala-Busse (Eds.), Lecture Notes in Artificial Intelligence (Vol. 9437, pp. 185–195). Presented at the 15th International conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2015), Cham, Switzerland: Springer.
Vancouver
1.
Jensen R, Vluymans S, Mac Parthaláin N, Cornelis C, Saeys Y. Semi-supervised fuzzy-rough feature selection. In: Yao Y, Hu Q, Yu H, Grzymala-Busse JW, editors. Lecture Notes in Artificial Intelligence. Cham, Switzerland: Springer; 2015. p. 185–95.
MLA
Jensen, Richard, Sarah Vluymans, Neil Mac Parthaláin, et al. “Semi-supervised Fuzzy-rough Feature Selection.” Lecture Notes in Artificial Intelligence. Ed. Yiyu Yao et al. Vol. 9437. Cham, Switzerland: Springer, 2015. 185–195. Print.
@inproceedings{6994757,
  abstract     = {With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90\% of the data object labels are missing.},
  author       = {Jensen, Richard and Vluymans, Sarah and Mac Parthal{\'a}in, Neil and Cornelis, Chris and Saeys, Yvan},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  editor       = {Yao, Yiyu and Hu, Qinghua and Yu, Hong and Grzymala-Busse, Jerzy W},
  isbn         = {9783319257822},
  issn         = {0302-9743},
  language     = {eng},
  location     = {Tianjin, China},
  pages        = {185--195},
  publisher    = {Springer},
  title        = {Semi-supervised fuzzy-rough feature selection},
  url          = {http://dx.doi.org/10.1007/978-3-319-25783-9\_17},
  volume       = {9437},
  year         = {2015},
}

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