Adaptation-Based Classifiers

While data transformation is a relatively straightforward way to do multilabel classification through traditional classifiers, an alternative approach based on adapting those classifiers to tackle the original multilabeled data also has been also explored. This chapter aims to introduce many of these method adaptations. Most of them rely on traditional algorithms based on the trees, neural networks, instance-based learning, etc. A general overview of them is provided in Sect. 5.1. Then, about thirty different proposals are detailed in Sects. 5.2–5.7, grouped according to the type of model they are founded on. A selection of four algorithms are experimentally tested in Sect. 5.8. Some final remarks are provided in Sect. 5.9.