Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies

In multi-label learning, 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. Common approaches to multi-label classification learn independent classifiers for each category, and perform ranking or thresholding schemes in order to obtain multi-label classification. In this paper, we describe an original method for multi-label classification problems derived from a Bayesian version of the K-nearest neighbor (KNN), and taking into account the dependencies between labels. Experiments on benchmark datasets show the usefulness and the efficiency of the proposed method compared to other existing methods.