Evolutionary Feature Manipulation in Data Mining/Big Data

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@Article{Xue:2017:sigevo,
  author =       "Bing Xue and Mengjie Zhang",
  title =        "Evolutionary Feature Manipulation in Data Mining/Big
                 Data",
  journal =      "SIGEvolution",
  year =         "2017",
  volume =       "10",
  number =       "1",
  pages =        "4--11",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sigevolution.org/issues/SIGEVOlution1001.pdf",
  size =         "8 pages",
  abstract =     "Known as the GIGO (Garbage In, Garbage Out) principle,
                 the quality of the input data highly influences or even
                 determines the quality of the output of any machine
                 learning, big data and data mining algorithm. The input
                 data which is often represented by a set of features
                 may suffer from many issues. Feature manipulation is an
                 effective means to improve the feature set quality, but
                 it is a challenging task. Evolutionary computation (EC)
                 techniques have shown advantages and achieved good
                 performance in feature manipulation. This paper reviews
                 recent advances on EC based feature manipulation
                 methods in classification, clustering, regression,
                 incomplete data, and image analysis, to provide the
                 community the state-of-the-art work in the field.",
}

Genetic Programming entries for Bing Xue Mengjie Zhang

Citations