Inducing Generalized Multi-Label Rules with Learning Classifier Systems
This work addresses multi-label classification problems, such as text classification and medical diagnoses, by providing an incremental improvement through a new rule format and algorithm adaptation.
The paper tackled multi-label classification by introducing a generalized rule format for flexible label-dependency modeling and adapting a Learning Classifier System framework, resulting in a novel algorithm that is competitive with state-of-the-art methods on benchmark datasets.
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. Although sparsely studied in this context, Learning Classifier Systems are naturally well-suited to multi-label classification problems, whose search space typically involves multiple highly specific niches. This is the motivation behind our current work that introduces a generalized multi-label rule format -- allowing for flexible label-dependency modeling, with no need for explicit knowledge of which correlations to search for -- and uses it as a guide for further adapting the general Michigan-style supervised Learning Classifier System framework. The integration of the aforementioned rule format and framework adaptations results in a novel algorithm for multi-label classification whose behavior is studied through a set of properly defined artificial problems. The proposed algorithm is also thoroughly evaluated on a set of multi-label datasets and found competitive to other state-of-the-art multi-label classification methods.