Uncertain Nearest Neighbor Classification

TL;DR

This paper introduces the Uncertain Nearest Neighbor (UNN) rule for classifying uncertain data, generalizing the deterministic nearest neighbor rule by focusing on class probabilities rather than individual objects, and demonstrates its effectiveness through experiments.

Contribution

It proposes a novel UNN rule that models uncertainty more accurately and provides an efficient algorithm for uncertain data classification.

Findings

UNN rule outperforms traditional methods in uncertain scenarios

Algorithm reduces computational cost significantly

Experimental results confirm effectiveness and efficiency

Abstract

This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest neighbor class of a test object is the class that maximizes the probability of providing its nearest neighbor. It is provided evidence that the former concept is much more powerful than the latter one in the presence of uncertainty, in that it correctly models the right semantics of the nearest neighbor decision rule when applied to the uncertain scenario. An effective and efficient algorithm to perform uncertain nearest neighbor classification of a generic (un)certain test object is designed, based on properties that greatly reduce the temporal cost associated with nearest neighbor class probability computation. Experimental results are presented, showing that the UNN rule is effective and efficient in classifying uncertain data.