Using Artificial Datasets to Analyze How Cardinality and Density Influence Multi-label Learning

In multi-label datasets, the number of labels associated with each instance is an important feature to be observed. Two relevant characteristics related to datasets´ number of labels are cardinality and density. In this work, we use artificial datasets generated through a framework named Mldatagem, freely-available in the internet. This framework enables configuring some other characteristics of the generated datasets. In this paper we present a study that analyze how and when distinct characteristics of the datasets influence the performance of multi-label learning methods.