Ensemble Based Classifiers
Classification methods founded on training several models with a certain heterogeneity degree, and then aggregating their predictions according to a particular strategy tends to be a very effective solution. Ensembles have been also used to tackle some specific obstacles, such as imbalanced class distribution. The goal in this chapter is to present several multilabel ensemble based solutions. Section 6.1 introduces this approach. Ensembles of binary classifiers are described in Sect. 6.2, while those based on multiclass methods are outlined in Sect. 6.3. Other kinds of ensembles will be briefly portrayed in Sect. 6.4. Some of these solutions are experimentally tested in Sect. 6.5, analyzing their predictive performance and running time. Lastly, Sect. 6.6 summarizes the chapter.