Using Interval Trees for Approximate Indexing of Instances

This paper presents a simple and effective method for approximate indexing of instances for instance based learning. The method uses an interval tree to determine a good starting search point for the nearest neighbor. The search stops when an early stopping criterion is met. The method proved to be very effective especially when only the first nearest neighbor is required. Keywords—Instance based learning, interval trees, the knn algorithm, machine learning.