Using Artificial Datasets to Analyze How Cardinality and Density Influence Multi-label Learning
Rodrigo Magalhães Rodovalho, Flávia Bernardini · 2014
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.