Accuracy and specificity trade-off in k-nearest neighbors classification

Abstract

The k-NN rule is a simple, flexible and widely used nonparametric decision method, also connected to many problems in image classification and retrieval such as annotation and content-based search. As the number of classes increases and finer classification is considered (e.g. specific dog breed), high accuracy is often not possible in such challenging conditions, resulting in a system that will often suggest a wrong label. However, predicting a broader concept (e.g. dog) is much more reliable, and still useful in practice. Thus, sacrificing certain specificity for a more secure prediction is often desirable. This problem has been recently posed in terms of accuracy-specificity trade-off. In this paper we study the accuracy-specificity trade-off in k-NN classification, evaluating the impact of related techniques (posterior probability estimation and metric learning). Experimental results show that a proper combination of k-NN and metric learning can be very effective and obtain good performance.