Using Network Analysis to Improve Nearest Neighbor Classification of Non-network Data

Abstract

The nearest neighbor classifier is a powerful, straightforward, and very popular approach to solving many classification problems. It also enables users to easily incorporate weights of training instances into its model, allowing users to highlight more promising examples. Instance weighting schemes proposed to date were based either on attribute values or external knowledge. In this paper, we propose a new way of weighting instances based on network analysis and centrality measures. Our method relies on transforming the training dataset into a weighted signed network and evaluating the importance of each node using a selected centrality measure. This information is then transferred back to the training dataset in the form of instance weights, which are later used during nearest neighbor classification. We consider four centrality measures appropriate for our problem and empirically evaluate our proposal on 30 popular, publicly available datasets. The results show that the proposed instance weighting enhances the predictive performance of the nearest neighbor algorithm.