Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

@article{Tahir2007SimultaneousFS,
  title={Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier},
  author={Muhammad Atif Tahir and Ahmed Bouridane and Fatih Kurugollu},
  journal={Pattern Recognition Letters},
  year={2007},
  volume={28},
  pages={438-446}
}
Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN… CONTINUE READING
Highly Cited
This paper has 382 citations. REVIEW CITATIONS
77 Citations
29 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 77 extracted citations

383 Citations

020406080'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 383 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 29 references

General iterative algorithms for combinatorial optimization

  • S. M. Sait, H. Youssef
  • IEEE Computer Society
  • 1999
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…