Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting

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

  title =        "Meta-Learning and Feature Ranking Using Genetic
                 Programming for Classification: Variable Terminal
  author =       "Anna Friedlander and Kourosh Neshatian and 
                 Mengjie Zhang",
  pages =        "940--947",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, GP, feature
                 ranking algorithms, feature selection, feature
                 weighting vector, learning classification, meta
                 learning, online feature weighting method, probability,
                 variable terminal weighting, feature extraction,
                 learning (artificial intelligence), probability",
  DOI =          "doi:10.1109/CEC.2011.5949719",
  abstract =     "We propose an online feature weighting method for
                 classification by genetic programming (GP). GP's
                 implicit feature selection was used to construct a
                 feature weighting vector, based on the fitness of
                 solutions in which the features were found and the
                 frequency at which they were found. The vector was used
                 to perform feature ranking and to perform meta-learning
                 by biasing terminal selection in mutation. The proposed
                 meta-learning mechanism significantly improved the
                 quality of solutions in terms of classification
                 accuracy on an unseen test set. The probability of
                 success---the probability of finding the desired
                 solution within a given number of generations (fitness
                 evaluations)---was also higher than canonical GP. The
                 ranking obtained by using the GP-provided feature
                 weighting was very highly correlated with the ranking
                 obtained by commonly-used feature ranking algorithms.
                 Population information during evolution can help shape
                 search behaviour (meta-learning) and obtain useful
                 information about the problem domain such as the
                 importance of input features with respect to each
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",

Genetic Programming entries for Anna Friedlander Kourosh Neshatian Mengjie Zhang