Properties of a GP active learning framework for streaming data with class imbalance

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Sara Khanchi and Malcolm I. Heywood and 
                 A. Nur Zincir-Heywood",
  title =        "Properties of a {GP} active learning framework for
                 streaming data with class imbalance",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "945--952",
  URL =          "",
  DOI =          "doi:10.1145/3071178.3071213",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming",
  month =        "15-19 " # jul,
  size =         "8 pages",
  abstract =     "Active learning algorithms attempt to interactively
                 develop a subset of data from which fitness evaluation
                 is performed. Moreover, the distribution of labelled
                 content within the data subset may adapt over time as
                 genetic programming (GP) individuals improve. The basic
                 goal is therefore to identify the most meaningful
                 subset of data to improve the current model. Under a
                 streaming data context additional challenges exist
                 relative to the non-streaming scenario: non-stationary
                 processes, partial observability any time operation.
                 This means that it is not possible to guarantee that
                 the content of the data subset even provides exemplars
                 for each class that could appear in the stream (i.e.,
                 different classes appear/disappear at different parts
                 of the stream). With this in mind, an investigation is
                 performed into the impact of adopting different
                 policies for controlling the development of data subset
                 content. To do so, a generic framework is defined in
                 terms of sampling and archiving policies. The resulting
                 evaluation under several large multi-class datasets
                 with class imbalance indicates that adopting random
                 sampling with a biased archiving policy is sufficient
                 for evolving GP classifiers that match or better the
                 current state-of-the-art, particularly when detecting
                 minor classes.",
  notes =        "Also known as \cite{Khanchi:2017:PGA:3071178.3071213,}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Sara Khanchi Malcolm Heywood Nur Zincir-Heywood