Evolving GP classifiers for streaming data tasks with concept change and label budgets: A benchmark study

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Vahdat:2015:hbgpa,
  author =       "Ali Vahdat and Jillian Morgan and 
                 Andrew R. McIntyre and Malcolm I. Heywood and Nur Zincir-Heywood",
  title =        "Evolving GP classifiers for streaming data tasks with
                 concept change and label budgets: A benchmark study",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "18",
  pages =        "451--480",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_18",
  abstract =     "Streaming data classification requires that several
                 additional challenges are addressed that are not
                 typically encountered in off-line supervised learning
                 formulations. Specifically, access to data at any
                 training generation is limited to a small subset of the
                 data, and the data itself is potentially generated by a
                 non-stationary process. Moreover, there is a cost to
                 requesting labels, thus a label budget is enforced.
                 Finally, an any-time classification requirement implies
                 that it must be possible to identify a champion
                 classifier for predicting labels as the stream
                 progresses. In this work, we propose a general
                 framework for deploying genetic programming (GP) to
                 streaming data classification under these constraints.
                 The framework consists of a sampling policy and an
                 archiving policy that enforce criteria for selecting
                 data to appear in a data subset. Only the exemplars of
                 the data subset are labeled, and it is the content of
                 the data subset that training epochs are performed
                 against. Specific recommendations include support for
                 GP task decomposition/modularity and making additional
                 training epochs per data subset. Both recommendations
                 make significant improvements to the baseline
                 performance of GP under streaming data with label
                 budgets. Benchmarking issues addressed include the
                 identification of datasets and performance measures.",
}

Genetic Programming entries for Ali Vahdat Jillian Morgan Andrew R McIntyre Malcolm Heywood Nur Zincir-Heywood

Citations