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

de Sá, Alex G. C. and Freitas, Alex A. and Pappa, Gisele L. (2018) Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming. In: Auger, Anne and Fonseca, Carlos M. and Lourenço, Nuno and Machado, Penousal and Paquete, Luís and Whitley, Darrell, eds. Parallel Problem Solving from Nature – PPSN XV. Lecture Notes in Computer Science . Springer, pp. 308-320. ISBN 978-3-319-99258-7. (doi:10.1007/978-3-319-99259-4_25) (KAR id:68970)


This paper proposes Auto-MEKAGGP, 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. Auto-MEKAGGP 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. Auto-MEKAGGP was tested in 10 datasets and compared to two well-known MLC methods, namely Binary Relevance and Classifier Chain, and also compared to GA-AutoMLC, a genetic algorithm we recently proposed for the same task. Two versions of Auto-MEKAGGP 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 Auto-MEKAGGP achieved the best predictive accuracy among all five evaluated methods, being the winner in six out of the 10 datasets.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-99259-4_25
Uncontrolled keywords: Automated machine learning (Auto-ML), Multi-label classification, Grammar-based genetic programming
Subjects: Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 06 Sep 2018 14:22 UTC
Last Modified: 09 Dec 2022 00:21 UTC
Resource URI: (The current URI for this page, for reference purposes)

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