kNN Classification with an Outlier Informative Distance Measure

Classification accuracy of the kNN algorithm is found to be adversely affected by the presence of outliers in the experimental datasets. An outlier score based on rank difference can be assigned to the points in these datasets by taking into consideration the distance and density of their local neighborhood points. In the present work, we introduce a generalized outlier informative distance measure where a factor based on the above score is used to modulate any potential distance function. Properties of the new outlier informative distance measure are presented. Experiments on several numeric datasets in the UCI machine learning repository clearly reveal the effectiveness of the proposed formulation.