- author = "Alex {de Sa} and Alex Freitas and Gisele Pappa",
- title = "Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-based Genetic Programming",
- booktitle = "15th International Conference on Parallel Problem Solving from Nature",
- year = "2018",
- editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley",
- volume = "11102",
- series = "LNCS",
- pages = "308--320",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Automated machine learning (Auto-ML), Multi-label classification, Grammar-based genetic programming",
- isbn13 = "978-3-319-99258-7",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99259-4_25",
- abstract = "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-Auto-MLC, 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.",
-
notes = "PPSN2018 http://ppsn2018.dei.uc.pt
This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018",
