Adaptive kNN using expected accuracy for classification of geo-spatial data

@article{Kibanov2018AdaptiveKU,
  title={Adaptive kNN using expected accuracy for classification of geo-spatial data},
  author={Mark Kibanov and Martin Becker and Juergen Mueller and Martin Atzm{\"u}ller and Andreas Hotho and Gerd Stumme},
  journal={Proceedings of the 33rd Annual ACM Symposium on Applied Computing},
  year={2018}
}
  • Mark Kibanov, Martin Becker, +3 authors Gerd Stumme
  • Published in SAC '18 2018
  • Computer Science
  • Proceedings of the 33rd Annual ACM Symposium on Applied Computing
  • The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e. g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We… CONTINUE READING

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.

    An adaptive k-nearest neighbor algorithm

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL