Methods to Edit Multi-label Training Sets Using Rough Sets Theory

Abstract

In multi-label classification problems, instances can be associated with several decision classes (labels) simultaneously. One of the most successful algorithms to deal with this kind of problem is the MLkNN method, which is lazy learner adapted to the multi-label scenario. All the computational models that realize inferences from examples have the common problem of the selection of those examples that should be included into the training set to increase the algorithm’s efficiency. This problem in known as training sets edition. Despite the extensive work in multi-label classification, there is a lack of methods for editing multi-label training sets. In this research, we propose three reduction techniques for editing multi-label training sets that rely on the Rough Set Theory. The simulations show that these methods reduce the number of examples in the training sets without affecting the overall performance, while in some case the performance is even improved.