Effective rule-based multi-label classification with learning classifier systems

Abstract

In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications such as text classification and medical diagnoses. However, rule-based methods, and especially Learning Classifier Systems (LCS), for tackling such problems have only been sparsely studied. This is the motivation behind our current work that introduces a generalized multi-label rule format and uses it as a guide for further adapting the general Michigan-style LCS framework. The resulting LCS algorithm is thoroughly evaluated and found competitive to other state-of-the-art multi-label classification methods. © 2013 Springer-Verlag Berlin Heidelberg.