Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming

Alex Guimarães Cardoso de Sá, Alex Alves Freitas, Gisele L. Pappa

Abstract: This paper proposes $$\text {Auto-MEKA}_{\text {GGP}}$$ , an Automated Machine Learning (Auto-ML) method for Multi-Label Classification (MLC) based on the MEKA tool, which offers a number of MLC algorithms. In MLC, each example can be associated with one or more class labels, making MLC problems harder than conventional (single-label) classification problems. Hence, it is essential to select an MLC algorithm and its configuration tailored (optimized) for the input dataset. $$\text {Auto-MEKA}_{\text {GGP}}$$ addresses this problem with two key ideas. First, a large number of choices of MLC algorithms and configurations from MEKA are represented into a grammar. Second, our proposed Grammar-based Genetic Programming (GGP) method uses that grammar to search for the best MLC algorithm and configuration for the input dataset. $$\text {Auto-MEKA}_{\text {GGP}}$$ was tested in 10 datasets and compared to two well-known MLC methods, namely Binary Relevance and Classifier Chain, and also compared to GA-Auto-MLC, a genetic algorithm we recently proposed for the same task. Two versions of $$\text {Auto-MEKA}_{\text {GGP}}$$ were tested: a full version with the proposed grammar, and a simplified version where the grammar includes only the algorithmic components used by GA-Auto-MLC. Overall, the full version of $$\text {Auto-MEKA}_{\text {GGP}}$$ achieved the best predictive accuracy among all five evaluated methods, being the winner in six out of the 10 datasets.