Induction in multi-label text classification domains

Abstract

Automated classification of text documents has two distinctive aspects. First, each training or testing example can be labeled with more than two classes at the same time-this has serious consequences not only for the induction algorithms, but also for how we evaluate the performance of the induced classifier. Second, the examples are usually described by great many attributes, which makes induction from hundreds of thousands of training examples prohibitively expensive. Both issues have been addressed by recent machine-learning literature, but the behaviors of existing solutions in real-world domains are still far from satisfactory. Here, we describe our own technique and report experiments with a concrete text database. © 2010 Springer-Verlag Berlin Heidelberg.