Validation Based Modified K-Nearest Neighbor

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.