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

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{deSa:2018:PPSN,
  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",
}

Genetic Programming entries for Alex G C de Sa Alex Alves Freitas Gisele L Pappa

Citations