Nearest neighbor selection for iteratively kNN imputation

  title={Nearest neighbor selection for iteratively kNN imputation},
  author={Shichao Zhang},
  journal={Journal of Systems and Software},
Existing kNN imputation methods for dealing with missing data are designed according to Minkowski distance or its variants, and have been shown to be generally efficient for numerical variables (features, or attributes). To deal with heterogeneous (i.e., mixed-attributes) data, we propose a novel kNN (k nearest neighbor) imputation method to iteratively imputing missing data, named GkNN (gray kNN) imputation. GkNN selects k nearest neighbors for each missing datum via calculating the gray… CONTINUE READING
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