A Survey on Evolutionary Computation Approaches to Feature Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Bing Xue and Mengjie Zhang and Will N. Browne and 
                 Xin Yao",
  title =        "A Survey on Evolutionary Computation Approaches to
                 Feature Selection",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2016",
  volume =       "20",
  number =       "4",
  pages =        "606--626",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2015.2504420",
  size =         "21 pages",
  abstract =     "Feature selection is an important task in data mining
                 and machine learning to reduce the dimensionality of
                 the data and increase the performance of an algorithm,
                 such as a classification algorithm. However, feature
                 selection is a challenging task due mainly to the large
                 search space. A variety of methods have been applied to
                 solve feature selection problems, where evolutionary
                 computation (EC) techniques have recently gained much
                 attention and shown some success. However, there are no
                 comprehensive guidelines on the strengths and
                 weaknesses of alternative approaches. This leads to a
                 disjointed and fragmented field with ultimately lost
                 opportunities for improving performance and successful
                 applications. This paper presents a comprehensive
                 survey of the state-of-the-art work on EC for feature
                 selection, which identifies the contributions of these
                 different algorithms. In addition, current issues and
                 challenges are also discussed to identify promising
                 areas for future research.",
  notes =        "p606 'only genetic programming (GP) and learning
                 classifier systems (LCSs) are able to perform embedded
                 feature selection'

                 also known as \cite{7339682}",

Genetic Programming entries for Bing Xue Mengjie Zhang Will N Browne Xin Yao