A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule

Abstract

In multilabel classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly used approach for multilabel classification is where a binary classifier is learned independently for each possible class. However, multilabeled data generally exhibit relationships between labels, and this approach fails to take such relationships into account. In this paper, we describe an original method for multilabel classification problems derived from a Bayesian version of the k-nearest neighbor (k-NN) rule. The method developed here is an improvement on an existing method for multilabel classification, namely multilabel k-NN, which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods.

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