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Fuzzy rough sets for self-labelling : an exploratory analysis

Sarah Vluymans UGent, Neil Mac Parthaláin, Chris Cornelis UGent and Yvan Saeys UGent (2016) 2016 IEEE International conference on fuzzy systems (FUZZ-IEEE) . In IEEE International Fuzzy Systems Conference Proceedings p.931-938
abstract
Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group of semi-supervised classification approaches are those known as self-labelling techniques, which attempt to assign class labels to the unlabelled data instances. This is achieved by using the class predictions based upon the information of the labelled part of the data. In this paper, the applicability and suitability of fuzzy rough set theory for the task of self-labelling is investigated. An important preparatory experimental study is presented that evaluates how accurately different fuzzy rough set models can predict the classes of unlabelled data instances for semi-supervised classification. The predictions are made either by considering only the labelled data instances or by involving the unlabelled data instances as well. A stability analysis of the predictions also helps to provide further insight into the characteristics of the different fuzzy rough models. Our study shows that the ordered weighted average based fuzzy rough model performs best in terms of both accuracy and stability. Our conclusions offer a solid foundation and rationale that will allow the construction of a fuzzy rough self-labelling technique. They also provide an understanding of the applicability of fuzzy rough sets for the task of semi-supervised classification in general.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
AGGREGATION, OPERATORS
in
2016 IEEE International conference on fuzzy systems (FUZZ-IEEE)
series title
IEEE International Fuzzy Systems Conference Proceedings
pages
931 - 938
publisher
IEEE
place of publication
New York, NY, USA
conference name
IEEE International conference on Fuzzy Systems (FUZZ-IEEE)
conference location
Vancouver, BC, Canada
conference start
2016-07-24
conference end
2016-07-29
Web of Science type
Proceedings Paper
Web of Science id
000392150700129
ISSN
1544-5615
ISBN
9781509006250
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8510056
handle
http://hdl.handle.net/1854/LU-8510056
date created
2017-02-17 16:03:35
date last changed
2017-05-09 13:52:39
@inproceedings{8510056,
  abstract     = {Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group of semi-supervised classification approaches are those known as self-labelling techniques, which attempt to assign class labels to the unlabelled data instances. This is achieved by using the class predictions based upon the information of the labelled part of the data. In this paper, the applicability and suitability of fuzzy rough set theory for the task of self-labelling is investigated. An important preparatory experimental study is presented that evaluates how accurately different fuzzy rough set models can predict the classes of unlabelled data instances for semi-supervised classification. The predictions are made either by considering only the labelled data instances or by involving the unlabelled data instances as well. A stability analysis of the predictions also helps to provide further insight into the characteristics of the different fuzzy rough models. Our study shows that the ordered weighted average based fuzzy rough model performs best in terms of both accuracy and stability. Our conclusions offer a solid foundation and rationale that will allow the construction of a fuzzy rough self-labelling technique. They also provide an understanding of the applicability of fuzzy rough sets for the task of semi-supervised classification in general.},
  author       = {Vluymans, Sarah and Mac Parthal{\'a}in, Neil and Cornelis, Chris and Saeys, Yvan},
  booktitle    = {2016 IEEE International conference on fuzzy systems (FUZZ-IEEE) },
  isbn         = {9781509006250},
  issn         = {1544-5615},
  keyword      = {AGGREGATION,OPERATORS},
  language     = {eng},
  location     = {Vancouver, BC, Canada},
  pages        = {931--938},
  publisher    = {IEEE},
  title        = {Fuzzy rough sets for self-labelling : an exploratory analysis},
  year         = {2016},
}

Chicago
Vluymans, Sarah, Neil Mac Parthaláin, Chris Cornelis, and Yvan Saeys. 2016. “Fuzzy Rough Sets for Self-labelling : an Exploratory Analysis.” In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , 931–938. New York, NY, USA: IEEE.
APA
Vluymans, S., Mac Parthaláin, N., Cornelis, C., & Saeys, Y. (2016). Fuzzy rough sets for self-labelling : an exploratory analysis. 2016 IEEE International conference on fuzzy systems (FUZZ-IEEE) (pp. 931–938). Presented at the IEEE International conference on Fuzzy Systems (FUZZ-IEEE), New York, NY, USA: IEEE.
Vancouver
1.
Vluymans S, Mac Parthaláin N, Cornelis C, Saeys Y. Fuzzy rough sets for self-labelling : an exploratory analysis. 2016 IEEE International conference on fuzzy systems (FUZZ-IEEE) . New York, NY, USA: IEEE; 2016. p. 931–8.
MLA
Vluymans, Sarah, Neil Mac Parthaláin, Chris Cornelis, et al. “Fuzzy Rough Sets for Self-labelling : an Exploratory Analysis.” 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . New York, NY, USA: IEEE, 2016. 931–938. Print.