Efficient Local Flexible Nearest Neighbor Classification

The nearest neighbor technique is a simple and appealing method to address classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a local adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, and therefore to minimize the bias of estimates. We propose a technique that computes a locally flexible metric by means of Support Vector Machines (SVMs). The maximum margin boundary found by the SVM is used to determine the most discriminant direction over the query’s neighborhood. Such direction provides a local weighting scheme for input features. We present experimental evidence, together with a formal justification, of classification performance improvement over the SVM algorithm alone and over a variety of adaptive learning schemes, by using both simulated and real data sets. Moreover, the proposed method has the important advantage of superior efficiency over the most competitive technique used in our experiments.