A new algorithm for fast search of the k nearest patterns

Abstract

The computational cost associated to the k-nearest neighbor classifier depends on the amount of available patterns, which makes this method impractical in many real-time applications. This fact makes interesting the study of fast algorithms for finding the k-nearest patterns, like, for example, the kLAESA algorithm. In this paper we propose a novel algorithm for finding the k-nearest patterns, denominated k-tuned approximating and eliminating search algorithm (kTAESA). The algorithm is used to implement kNN classifiers, which are applied to three databases from the UCI machine learning benchmark repository. Results are compared with those achieved by the exhaustive search, the kAESA and the kLAESA algorithms, in terms of number of distances to evaluate, number of simple operations (sums, comparisons and products) needed to classify each pattern, and amount of required memory. Results demonstrate the best performance of the proposal, mainly when the number of operations is considered.