Inducing Generalized Multi-Label Rules with Learning Classifier Systems

TL;DR

This paper introduces a generalized multi-label rule format and adapts Learning Classifier Systems to effectively handle multi-label classification, demonstrating competitive performance on various datasets.

Contribution

It presents a novel multi-label rule format and integrates it into Learning Classifier Systems, enabling flexible label dependency modeling without prior knowledge.

Findings

The new algorithm performs competitively with state-of-the-art methods.

The generalized rule format effectively captures label dependencies.

The approach is validated on artificial and real-world datasets.

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. 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.