Feature selection for multi-label learning

Feature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In multi-label learning, label dependence is considered another aspect that can contribute to improve learning performance. A replicable and wide systematic review performed by us corroborates this idea. Based on this information, it is believed that considering label dependence during feature selection can lead to better learning performance. The hypothesis of this work is that multi-label feature selection algorithms that consider label dependence will perform better than the ones that disregard it. To this end, we propose multi-label feature selection algorithms that take into account label relations. These algorithms were experimentally compared to the standard approach for feature selection, showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.