Feature extraction for multi-label learning in the domain of email classification
José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Rafael Morales-Bueno · 2011
Multi-label learning is a very interesting field in Machine Learning. It allows to generalise standard methods and evaluation procedures, and tackle challenging real problems where one example can be tagged with more than one label. In this paper we study the performance of different multi-label methods in combination with standard single-label algorithms, using several specific multi-label metrics. What we want to show is how a good preprocessing phase can improve the performance of such methods and algorithms. As we will explain, its main advantage is a shorter time to induce the models, while keeping (even improving) other classification quality measures. We use the GNUsmail framework to do the preprocessing of an existing and extensively used dataset, to obtain a reduced feature space that conserves the relevant information and allows improvements on performance. Thanks to the capabilities of GNUsmail, the preprocessing step can be easily applied to different email datasets.