Cardinality and Density Measures and Their Influence to Multi-Label Learning Methods
Flávia Bernardini, Rodrigo Barbosa da Silva, Rodrigo Magalhães Rodovalho, Edwin Benito Mitacc Meza · Learning and Nonlinear Models · 2014
Two main characteristics of multi-label dataset are cardinality and density, related to the number of labels of (each instance of) a multi-label dataset. The relation between these characteristics and multi-label learning performance has been observed with different datasets. However, the difference in domain dataset attributes also interfere on multi-label learning performance. In this work, we use a real dataset, named The Million Song Dataset, available in the internet, which presents the property of having too many labels associated to their instances (songs), as well as so many instances. In this work we present the processed datasets used to conduct our experiments, and we also describe our experiments results.