Multi-Objective Genetic Programming for Dataset Similarity Induction

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Smid:2015:ieeeSSCI,
  author =       "Jakub Smid and Martin Pilat and Klara Peskova and 
                 Roman Neruda",
  booktitle =    "2015 IEEE Symposium Series on Computational
                 Intelligence",
  title =        "Multi-Objective Genetic Programming for Dataset
                 Similarity Induction",
  year =         "2015",
  pages =        "1576--1582",
  month =        "7-10 " # dec,
  address =      "Cape Town, South Africa",
  keywords =     "genetic algorithms, genetic programming, Metadata,
                 Measurement, Optimization, Prediction algorithms,
                 Correlation, Electronic mail",
  isbn13 =       "978-1-4799-7560-0",
  DOI =          "doi:10.1109/SSCI.2015.222",
  size =         "7 pages",
  abstract =     "Metal earning - the recommendation of a suitable
                 machine learning technique for a given dataset - relies
                 on the concept of similarity between datasets.
                 Traditionally, similarity measures have been
                 constructed manually, and thus could not precisely
                 grasp the complex relationship among the different
                 features of the datasets. Recently, we have used an
                 attribute alignment technique combined with genetic
                 programming to obtain more fine-grained and trainable
                 dataset similarity measure. In this paper, we propose
                 an approach based on multi-objective genetic
                 programming for evolving an attribute similarity
                 function. Multi-objective optimisation is used to
                 encourage some of the metric properties, thus
                 contributing to the generalisation abilities of the
                 similarity function being evolved. Experiments are
                 performed on the data extracted from the OpenML
                 repository and their results are compared to the
                 baseline algorithm.",
  notes =        "Also known as \cite{7376798}",
}

Genetic Programming entries for Jakub Smid Martin Pilat Klara Peskova Roman Neruda

Citations