Kernelized Fuzzy Rough Sets and Their Applications

@article{Hu2011KernelizedFR,
  title={Kernelized Fuzzy Rough Sets and Their Applications},
  author={Qinghua Hu and Daren Yu and Witold Pedrycz and Degang Chen},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2011},
  volume={23},
  pages={1649-1667}
}
Kernel machines and rough sets are two classes of commonly exploited learning techniques. Kernel machines enhance traditional learning algorithms by bringing opportunities to deal with nonlinear classification problems, rough sets introduce a human-focused way to deal with uncertainty in learning problems. Granulation and approximation play a pivotal role in rough sets-based learning and reasoning. However, a way how to effectively generate fuzzy granules from data has not been fully studied so… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 45 CITATIONS, ESTIMATED 39% COVERAGE

115 Citations

02040'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 115 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 54 REFERENCES

On the T-Transitivity of Kernels

  • B. Moser
  • Fuzzy Sets and Systems, vol. 157, pp. 1787-1796…
  • 2006
Highly Influential
5 Excerpts

Rough Fuzzy Sets and Fuzzy Rough Sets

  • D. Dubois, H. Prade
  • Int’l J. General Systems, vol. 17, nos. 2/3, pp…
  • 1990
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…