Dynamic selection of k nearest neighbors in instance-based learning
Carl Hulett, Andy J. Hall, Guangzhi Qu · 2012
kNN is a popular lazy-learning algorithm used for a wide variety of machine learning applications. One problem with this algorithm is the choice of k value. Different k values can have a large impact on the predictive accuracy of the algorithm, and picking a good value is generally unintuitive by looking at the data set. Cross-validation over multiple folds is often used to find the best value for k in kNN based on prediction results. In this paper, we propose automatic selection of neighboring instances as defined by a dynamic local region unique to each instance, as opposed to the traditional approach of considering the manually specified k nearest neighbors. Removing the need to select an appropriate k value removes the cross-validation step, which improves the computational performance of the algorithm. Classification accuracy achieved by this approach is only slightly lower than the results of using kNN with an optimally selected k value.