@article{584,
  author = {Zahra Rezaei, Alizadeh Hosein, Sajad Parvin, Alinejad-Rokny Hamid},
  title = {An Extended MKNN: Modified K-Nearest Neighbor},
  journal = {Journal of Networking Technology},
  year = {2011},
  volume = {2},
  number = {4},
  doi = {},
  url = {http://www.dline.info/jnt/fulltext/v2n4/4.pdf},
  abstract = {In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. The robust neighbors are detected using a validation process. This method is more robust than traditional equivalent methods. This new classification method is called Modified K-Nearest Neighbor. Inspired the traditional KNN algorithm, the main idea is classifying the test samples according to their neighbor tags. This method is a kind of weighted KNN so that these weights are determined using a different procedure. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the excellent improvement in accuracy in comparison with KNN method.},
}