Multi-view semi-supervised learning using genetic programming interpretable classification rules

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Carlos Garcia-Martinez and Sebastian Ventura",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Multi-view semi-supervised learning using genetic
                 programming interpretable classification rules",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "573--579",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Multi-view learning is a novel paradigm that aims at
                 obtaining better results by examining the information
                 from several perspectives instead of by analysing the
                 same information from a single viewpoint. The
                 multi-view methodology has widely been used for
                 semi-supervised learning, where just some patterns were
                 previously classified by an expert and there is a large
                 amount of unlabelled ones. However to our knowledge,
                 the multi-view learning paradigm has not been applied
                 to produce interpretable rule-based classifiers before.
                 In this work, we present a multi-view extension of a
                 grammar-based genetic programming model for inducing
                 rules for semi-supervised contexts. Its idea is to
                 evolve several populations, and their corresponding
                 views, favouring both the accuracy of the predictions
                 for the labelled patterns and the prediction agreement
                 with the other views for unlabelled ones. We have
                 carried out experiments with two to five views, on six
                 common datasets for fully-supervised learning that have
                 been partially anonymised for our semi-supervised
                 study. Our results show that the multi-view paradigm
                 allows to obtain slightly better rule-based
                 classifiers, and that two views becomes preferred.",
  keywords =     "genetic algorithms, genetic programming, learning
                 (artificial intelligence), pattern classification,
                 grammar-based genetic programming model, interpretable
                 classification rules, multiview semi-supervised
                 learning, rule-based classifiers, semi-supervised
                 contexts, Context, Kernel, Semisupervised learning,
                 Sociology, Statistics, Training",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969362",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as

Genetic Programming entries for Carlos Garcia-Martinez Sebastian Ventura