Advanced search
1 file | 331.78 KB
Author
Organization
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.
Keywords
AGGREGATION, OPERATORS

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 331.78 KB

Citation

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

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.
@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},
  language     = {eng},
  location     = {Vancouver, BC, Canada},
  pages        = {931--938},
  publisher    = {IEEE},
  title        = {Fuzzy rough sets for self-labelling : an exploratory analysis},
  year         = {2016},
}

Web of Science
Times cited: