Multi-Label Rule Learning

This project aimed at solving multi-label classification problems using rule learning algorithms. Multi-label classification has received a lot of attention in the recent machine learning literature and is nowadays used in applications as diverse as music categorization, semantic scene classification, or protein function classification. Multi-label classification becomes particularly challenging when it comes to discovering hidden dependencies between labels. As rules provide a natural form of expressing such dependencies, they are a natural choice to capture label correlations. The Lichtenberg cluster was used to evaluate the predictive accuracy of the developed algorithms and existing baselines using synthetic and realworld benchmark data sets.