Learning Instance-Level Label Correlation Distribution for Multilabel Classification With Fuzzy Rough Sets

In multilabel learning, research on label correlation provides an effective solution to compress the hypothesis space of classifiers. However, this article focus on the label correlation adapted to overall data, while ignoring the locally targeted information presented by some instances. The lack exploration on the distribution of local label correlation in multilabel instance space undoubtedly limits the in-depth application of label correlation in multilabel learning. Based on the fuzzy rough set theory, the instance-level label correlation distribution is first proposed in this article and applied to design a novel multilabel learner. For each multilabel instance, the local importance of features to label is quantitatively analyzed, by considering the decisive influence of input information on decision making. According to coincidence degree between local feature weight distribution for different labels, the instance-level label correlation is constructed. In order to reflect the internal relationship between label variables objectively, the instance-level label correlation distribution is integrated into the empirical label relevance. On the basis, the label relevance matrix is used to define the constraints of the optimization function in a new form. The relative position of subseparating hyperplanes in input space is quantitatively characterized to reduce the complexity of the multilabel classifier and improve the learning performance. The experiment results on 18 multilabel datasets illustrate the effectiveness of our algorithm. The impact of core parameters on the performance is also dissected.