Multi-objective Genetic Programming for Multiple Instance Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Amelia Zafra and Sebastian Ventura",
  title =        "Multi-objective Genetic Programming for Multiple
                 Instance Learning",
  booktitle =    "18th European Conference on Machine Learning, ECML
  year =         "2007",
  bibsource =    "DBLP,",
  editor =       "Joost N. Kok and Jacek Koronacki and 
                 Ramon L{\'o}pez de M{\'a}ntaras and Stan Matwin and Dunja Mladenic and 
                 Andrzej Skowron",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4701",
  pages =        "790--797",
  address =      "Warsaw, Poland",
  month =        sep # " 17-21",
  keywords =     "genetic algorithms, genetic programming, poster",
  isbn13 =       "978-3-540-74957-8",
  DOI =          "doi:10.1007/978-3-540-74958-5_81",
  size =         "8 pages",
  abstract =     "This paper introduces the use of multi-objective
                 evolutionary algorithms in multiple instance learning.
                 In order to achieve this purpose, a multi-objective
                 grammar-guided genetic programming algorithm (MOG3P-MI)
                 has been designed. This algorithm has been evaluated
                 and compared to other existing multiple instance
                 learning algorithms. Research on the performance of our
                 algorithm is carried out on two well-known drug
                 activity prediction problems, Musk and Mutagenesis,
                 both problems being considered typical benchmarks in
                 multiple instance problems. Computational experiments
                 indicate that the application of the MOG3P-MI algorithm
                 improves accuracy and decreases computational cost with
                 respect to other techniques.",
  notes =        "",

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura