A New Label Ordering Method in Classifier Chains Based on Imprecise Probabilities

In Multi-Label Classification (MLC), Classifier Chains (CC) are considered simple and effective methods to exploit correlations between labels. A CC considers a binary classifier per label, in which the previous labels, according to an established order, are used as additional features. The label order strongly influences the performance of the CC, and there is no way to determine the optimal order so far. In this work, a new label ordering method based on label correlations is proposed. It uses a non-parametric model based on imprecise probabilities to estimate the correlations between pairs of labels. Then, it employs a greedy procedure that, to insert the labels in the chain, considers the correlations among the candidate labels and the ones already inserted, as well as the correlations between the candidate labels and the ones non-inserted yet. We argue that our proposal presents some advantages over the label ordering methods in CC developed so far based on label correlations. It is also shown that our proposal achieves better experimental results than the label ordering methods proposed so far that use label correlations in CC. (C) 2022 Elsevier B.V. All rights reserved.