Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes

The Nearest Neighbor rule is one of the most successful classifiers in machine learning but it is very sensitive to noisy data, which may cause its performance to deteriorate. This contribution proposes a new feature weighting classifier that tries to reduce the influence of noisy features. The computation of the weights is based on combining imputation methods and non-parametrical statistical tests. The results obtained show that our proposal can improve the performance of the Nearest Neighbor classifier dealing with different types of noisy data.

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