A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Neshatian:2012:ieeeTEC,
  author =       "Kourosh Neshatian and Mengjie Zhang and 
                 Peter Andreae",
  title =        "A Filter Approach to Multiple Feature Construction for
                 Symbolic Learning Classifiers Using Genetic
                 Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2012",
  volume =       "16",
  number =       "5",
  pages =        "645--661",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming,
                 classification, decision trees, feature construction,
                 rule-based systems",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2011.2166158",
  size =         "17 pages",
  abstract =     "Feature construction is an effort to transform the
                 input space of classification problems in order to
                 improve the classification performance. Feature
                 construction is particularly important for classifier
                 inducers that cannot transform their input space
                 intrinsically. This article proposes GPMFC, a multiple
                 feature construction system for classification problems
                 using genetic programming (GP). The article takes a
                 non-wrapper approach by introducing a filter-based
                 measure of goodness for constructed features. The
                 constructed, high-level features are functions of
                 original input features. These functions are evolved by
                 GP using an entropy-based fitness function that
                 maximises the purity of class intervals. A decomposable
                 objective function is proposed so that the system is
                 able to construct multiple high-level features for each
                 problem. The constructed features are used to transform
                 the original input space to a new space with better
                 separability. Extensive experiments are conducted on a
                 number of benchmark problems and symbolic learning
                 classifiers. The results show that, in most cases, the
                 new approach is highly effective in increasing the
                 classification performance in rule-based and decision
                 tree classifiers. The constructed features help improve
                 the learning performance of symbolic learners. The
                 constructed features, however, may lack
                 intelligibility.",
  notes =        "Also known as \cite{6151112}",
}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang Peter Andreae

Citations