A Feature Transformation Method using Multiobjective Genetic Programming for Two-Class Classification

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

@InProceedings{Tomoyuki:2015:CEC,
  author =       "Hiroyasu Tomoyuki and Shiraishi Toshihide and 
                 Yoshida Tomoya and Yamamoto Utako",
  title =        "A Feature Transformation Method using Multiobjective
                 Genetic Programming for Two-Class Classification",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "2989--2995",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257261",
  abstract =     "In this paper, we investigate a method of performing
                 feature transformation on input data in a 1-dimensional
                 space in order to increase the accuracy of classifiers.
                 Through optimized feature transformation, it is
                 possible to create data which generate the models with
                 high accuracy than the original data. We use Genetic
                 Programming (GP) to find a feature transformation
                 function. We proposed evaluation functions using GP and
                 have been successful in finding transformation
                 functions with a high degree of accuracy. On the other
                 hand, where there is a deviation in the number of data
                 items belonging to multiple classes, classes with a
                 large number of data items are more accurate than those
                 that do not. In order to resolve this, referring to
                 existing research, we examined a method of handling the
                 problem of improving accuracy and correcting class
                 imbalanced accuracy from the generated models based on
                 multi-purpose optimization. We then investigated the
                 method of multi-purpose optimization and how to
                 determine the threshold for classification. The results
                 of the investigation were that we could obtain a
                 transformation function that was more accurate and
                 could consider the accuracy of multiple classes
                 simultaneously.",
  notes =        "1320 hrs 15569 CEC2015",
}

Genetic Programming entries for Tomoyuki Hiroyasu Toshihide Shiraishi Tomoya Yoshida Utako Yamamoto

Citations