Multi-Label Collective Classification (original) (raw)

Collective classification in relational data has become an important and active research topic in the last decade, where class labels for a group of linked instances are correlated and need to be predicted simultaneously. Collective classification has a wide variety of real world applications, e.g. hyperlinked document classification, social networks analysis and collaboration networks analysis. Current research on collective classification focuses on single-label settings, which assumes each instance can only be assigned with exactly one label among a finite set of candidate classes. However, in many real-world relational data, each instance can be assigned with a set of multiple labels simultaneously. In this paper, we study the problem of multi-label collective classification and propose a novel solution, called Icml (Iterative Classification of Multiple Labels), to effectively assign a set of multiple labels to each instance in the relational dataset. The proposed Icml model is able to capture the dependencies among the label sets for a group of related instances and the dependencies among the multiple labels within each label set simultaneously. Empirical studies on real-world tasks demonstrate that the proposed multi-label collective classification approach can effectively boost classification performances in multilabel relational datasets.