Evolutionary model building under streaming data for classification tasks: opportunities and challenges

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@Article{Heywood:2015:GPEM,
  author =       "Malcolm I. Heywood",
  title =        "Evolutionary model building under streaming data for
                 classification tasks: opportunities and challenges",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "3",
  pages =        "283--326",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Streaming
                 data, Non-stationary processes, Dynamic environment,
                 Imbalanced data, Task decomposition, Ensemble learning,
                 Active learning, Evolvability, Diversity, Memory",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9236-y",
  size =         "44 pages",
  abstract =     "Streaming data analysis potentially represents a
                 significant shift in emphasis from schemes historically
                 pursued for offline (batch) approaches to the
                 classification task. In particular, a streaming data
                 application implies that: (1) the data itself has no
                 formal start or end; (2) the properties of the process
                 generating the data are non-stationary, thus models
                 that function correctly for some part(s) of a stream
                 may be ineffective elsewhere; (3) constraints on the
                 time to produce a response, potentially implying an
                 anytime operational requirement; and (4) given the
                 prohibitive cost of employing an oracle to label a
                 stream, a finite labelling budget is necessary. The
                 scope of this article is to provide a survey of
                 developments for model building under streaming
                 environments from the perspective of both evolutionary
                 and non-evolutionary frameworks. In doing so, we bring
                 attention to the challenges and opportunities that
                 developing solutions to streaming data classification
                 tasks are likely to face using evolutionary
                 approaches.",
}

Genetic Programming entries for Malcolm Heywood

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