Semi-Supervised Fuzzy-Rough Feature Selection

  • Richard JensenEmail author
  • Sarah Vluymans
  • Neil Mac Parthaláin
  • Chris Cornelis
  • Yvan Saeys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)


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.


Fuzzy-rough sets Feature selection Semi-supervised learning 



Neil Mac Parthaláin would like to acknowledge the financial support for this research through NISCHR (National Institute for Social Care and Health Research) Wales, Grant reference: RFS-12-37. Sarah Vluymans is supported by the Special Research Fund (BOF) of Ghent University. Chris Cornelis was partially supported by the Spanish Ministry of Science and Technology under the project TIN2011-28488 and the Andalusian Research Plans P11-TIC-7765, P10-TIC-6858 and P12-TIC-2958.


  1. 1.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123 (1995)CrossRefGoogle Scholar
  2. 2.
    Cornelis, C., Jensen, R., Hurtado Martín, G., Ślȩzak, D.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Słowiński, R. (ed.) Intelligent Decision Support, pp. 203–232. Springer, Dordrecht (1992)CrossRefGoogle Scholar
  4. 4.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine, CA (2010).
  5. 5.
    Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)CrossRefGoogle Scholar
  6. 6.
    Jensen, R., Tuson, A., Shen, Q.: Finding rough and fuzzy-rough set reducts with SAT. Inf. Sci. 255, 100–120 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)CrossRefGoogle Scholar
  8. 8.
    Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets. Fuzzy Sets Syst. 126(2), 137–155 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Widz, S., Ślęzak, D.: Attribute Subset Quality Functions over a Universe of Weighted Objects. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 99–110. Springer, Heidelberg (2014) Google Scholar
  10. 10.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Richard Jensen
    • 1
    Email author
  • Sarah Vluymans
    • 2
    • 3
  • Neil Mac Parthaláin
    • 1
  • Chris Cornelis
    • 2
    • 4
  • Yvan Saeys
    • 3
    • 5
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwyth, Ceredigion, WalesUK
  2. 2.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium
  3. 3.VIB Inflammation Research CenterZwijnaardeBelgium
  4. 4.Department of Computer Science and AI CITIC-UGRUniversity of GranadaGranadaSpain
  5. 5.Department of Respiratory MedicineGhent UniversityGhentBelgium

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