Multi-objective genetic programming for feature extraction and data visualization

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  author =       "Alberto Cano and Sebastian Ventura and 
                 Krzysztof J. Cios",
  title =        "Multi-objective genetic programming for feature
                 extraction and data visualization",
  journal =      "Soft Computing",
  year =         "2017",
  volume =       "21",
  number =       "8",
  pages =        "2069--2089",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Feature extraction, Visualization",
  ISSN =         "1433-7479",
  DOI =          "doi:10.1007/s00500-015-1907-y",
  size =         "21 pages",
  abstract =     "Feature extraction transforms high-dimensional data
                 into a new subspace of lower dimensionality while
                 keeping the classification accuracy. Traditional
                 algorithms do not consider the multi-objective nature
                 of this task. Data transformations should improve the
                 classification performance on the new subspace, as well
                 as to facilitate data visualization, which has
                 attracted increasing attention in recent years.
                 Moreover, new challenges arising in data mining, such
                 as the need to deal with imbalanced data sets call for
                 new algorithms capable of handling this type of data.
                 This paper presents a Pareto-based multi-objective
                 genetic programming algorithm for feature extraction
                 and data visualization. The algorithm is designed to
                 obtain data transformations that optimize the
                 classification and visualization performance both on
                 balanced and imbalanced data. Six classification and
                 visualization measures are identified as objectives to
                 be optimized by the multi-objective algorithm. The
                 algorithm is evaluated and compared to 11 well-known
                 feature extraction methods, and to the performance on
                 the original high-dimensional data. Experimental
                 results on 22 balanced and 20 imbalanced data sets show
                 that it performs very well on both types of data, which
                 is its significant advantage over existing feature
                 extraction algorithms.",

Genetic Programming entries for Alberto Cano Rojas Sebastian Ventura Krzysztof J Cios