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

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  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

Genetic Programming entries for Malcolm Heywood