A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/hais/ZafraV09,
  title =        "A Comparison of Multi-objective Grammar-Guided Genetic
                 Programming Methods to Multiple Instance Learning",
  author =       "Amelia Zafra and Sebastian Ventura",
  booktitle =    "Proceedings of the 4th International Conference on
                 Hybrid Artificial Intelligence Systems, HAIS 2009",
  year =         "2009",
  editor =       "Emilio Corchado and Xindong Wu and Erkki Oja and 
                 {\'A}lvaro Herrero and Bruno Baruque",
  volume =       "5572",
  series =       "Lecture Notes in Computer Science",
  pages =        "450--458",
  address =      "Salamanca, Spain",
  month =        jun # " 10-12",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-02318-7",
  DOI =          "doi:10.1007/978-3-642-02319-4_54",
  bibdate =      "2009-06-24",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/hais/hais2009.html#ZafraV09",
  abstract =     "This paper develops a first comparative study of
                 multiobjective algorithms in Multiple Instance Learning
                 (MIL) applications. These algorithms use grammar-guided
                 genetic programming, a robust classification paradigm
                 which is able to generate understandable rules that are
                 adapted to work with the MIL framework. The algorithms
                 obtained are based on the most widely used and compared
                 multi-objective evolutionary algorithms. Thus, we
                 design and implement SPG3P-MI based on the Strength
                 Pareto Evolutionary Algorithm, NSG3P-MI based on the
                 Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI
                 based on the Multi-objective genetic local search.
                 These approaches are tested with different MIL
                 applications and compared to a previous singleobjective
                 grammar-guided genetic programming proposal. The
                 results demonstrate the excellent performance of
                 multi-objective approaches in achieving accurate models
                 and their ability to generate comprehensive rules in
                 the knowledgable discovery process.",
}

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura

Citations