Using genetic programming for context-sensitive feature scoring in classification problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/connection/NeshatianZ11,
  author =       "Kourosh Neshatian and Mengjie Zhang",
  title =        "Using genetic programming for context-sensitive
                 feature scoring in classification problems",
  journal =      "Connection Science",
  year =         "2011",
  number =       "3",
  volume =       "23",
  pages =        "183--207",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, feature
                 scoring, feature ranking, feature selection,
                 classification",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/09540091.2011.630065",
  URL =          "http://www.tandfonline.com/doi/pdf/10.1080/09540091.2011.630065",
  DOI =          "doi:10.1080/09540091.2011.630065",
  size =         "25 pages",
  abstract =     "Feature scoring is an avenue to feature selection that
                 provides a measure of usefulness for the individual
                 features of a classification task. Features are ranked
                 based on their scores and selection is performed by
                 choosing a small group of high-ranked features. Most
                 existing feature scoring/ranking methods focus on the
                 relevance of a single feature to the class labels
                 regardless of the role of other features
                 (context-insensitive). The paper proposes a genetic
                 programming (GP)-based method to see how a set of
                 features can contribute towards discriminating
                 different classes. The features receive score in the
                 context of other features participating in a GP
                 program. The scoring mechanism is based on the
                 frequency of appearance of each feature in a collection
                 of GP programs and the fitness of those programs. Our
                 results show that the proposed feature ranking method
                 can detect important features of a problem. A variety
                 of different classifiers restricted to just a few of
                 these high-ranked features work well. The proposed
                 scoring-ranking mechanism can also shrink the search
                 space of size O(2 n ) of subsets of features to a
                 search space of size O(n) in which there are points
                 that are very likely to improve the classification
                 performance.",
  bibdate =      "2011-12-01",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/connection/connection23.html#NeshatianZ11",
}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang

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