Unsupervised Elimination of Redundant Features Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Kourosh Neshatian and Mengjie Zhang",
  title =        "Unsupervised Elimination of Redundant Features Using
                 Genetic Programming",
  booktitle =    "Proceedings of the 22nd Australasian Joint Conference
                 on Artificial Intelligence (AI'09)",
  year =         "2009",
  editor =       "Ann E. Nicholson and Xiaodong Li",
  volume =       "5866",
  series =       "Lecture Notes in Computer Science",
  pages =        "432--442",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  address =      "Melbourne, Australia",
  month =        dec # " 1-4",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-10438-1",
  DOI =          "doi:10.1007/978-3-642-10439-8_44",
  abstract =     "While most feature selection algorithms focus on
                 finding relevant features, few take the redundancy
                 issue into account. We propose a nonlinear redundancy
                 measure which uses genetic programming to find the
                 redundancy quotient of a feature with respect to a
                 subset of features. The proposed measure is
                 unsupervised and works with unlabeled data. We
                 introduce a forward selection algorithm which can be
                 used along with the proposed measure to perform feature
                 selection over the output of a feature ranking
                 algorithm. The effectiveness of the proposed method is
                 assessed by applying it to the output of the Chi-square
                 feature ranker on a classification task. The results
                 show significant improvements in the performance of
                 decision tree and SVM classifiers.",

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang