Efficient \(k\)-NN classification based on homogeneous clusters

摘要

The \(k\)-NN classifier is a widely used classification algorithm. However, exhaustively searching the whole dataset for the nearest neighbors is prohibitive for large datasets because of the high computational cost involved. The paper proposes an efficient model for fast and accurate nearest neighbor classification. The model consists of a non-parametric cluster-based preprocessing algorithm that constructs a two-level speed-up data structure and algorithms that access this structure to perform the classification. Furthermore, the paper demonstrates how the proposed model can improve the performance on reduced sets built by various data reduction techniques. The proposed classification model was evaluated using eight real-life datasets and compared to known speed-up methods. The experimental results show that it is a fast and accurate classifier, and, in addition, it involves low pre-processing computational cost.