On the application of GP to streaming data classification tasks with label budgets

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

@InProceedings{Vahdat:2014:GECCOcomp,
  author =       "Ali Vahdat and Aaron Atwater and 
                 Andrew R. McIntyre and Malcolm I. Heywood",
  title =        "On the application of GP to streaming data
                 classification tasks with label budgets",
  booktitle =    "GECCO 2014 Workshop on Evolutionary Computation for
                 Big Data and Big Learning",
  year =         "2014",
  editor =       "Jaume Bacardit and Ignacio Arnaldo and 
                 Kalyan Veeramachaneni and Una-May O'Reilly",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1287--1294",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2611385",
  DOI =          "doi:10.1145/2598394.2611385",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A framework is introduced for applying GP to streaming
                 data classification tasks under label budgets. This is
                 a fundamental requirement if GP is going to adapt to
                 the challenge of streaming data environments. The
                 framework proposes three elements: a sampling policy, a
                 data subset and a data archiving policy. The sampling
                 policy establishes on what basis data is sampled from
                 the stream, and therefore when label information is
                 requested. The data subset is used to define what GP
                 individuals evolve against. The composition of such a
                 subset is a mixture of data forwarded under the
                 sampling policy and historical data identified through
                 the data archiving policy. The combination of sampling
                 policy and the data subset achieve a decoupling between
                 the rate at which the stream passes and the rate at
                 which evolution commences. Benchmarking is performed on
                 two artificial data sets with specific forms of sudden
                 shift and gradual drift as well as a well known
                 real-world data set.",
  notes =        "Also known as \cite{2611385} Distributed at
                 GECCO-2014.",
}

Genetic Programming entries for Ali Vahdat Aaron Atwater Andrew R McIntyre Malcolm Heywood

Citations