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

Abstract

In multi-label learning, research on label correlation provides an effective solution to compress the hypothesis space of classifiers. However, the current works 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 multi-label instance space undoubtedly limits the in-depth application of label correlation in multi-label learning. Based on fuzzy rough set theory, the instance-level label correlation distribution is first proposed in this paper and applied to design a novel multi-label learner. For each multi-label 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 optimization function in a new form. The relative position of sub-separating hyperplanes in input space is quantitatively characterized to reduce the complexity of multi-label classifier and improve learning performance. The experiment results on eighteen multi-label datasets illustrate the effectiveness of our algorithm. The impact of core parameters on performance is also dissected.