Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers
Redundant features may deteriorate the performance of the Nearest Neighbor rule. This paper proposes a new feature weighting classifier to overcome this problem. Imputation methods are used to estimate a distribution of values for each feature. A statistical test sets weights based on differences of the feature distributions. Our proposal outperforms the rest of the classifiers considered in the comparisons.
feature weighting nearest neighbor imputation methods classification