On sampling strategies for small and continuous data with the modeling of genetic programming and adaptive neuro-fuzzy inference system

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

@Article{journals/jifs/SenSGY12,
  author =       "S. Sen and Ebru Akcapinar Sezer and 
                 Candan Gokceoglu and Saffet Yagiz",
  title =        "On sampling strategies for small and continuous data
                 with the modeling of genetic programming and adaptive
                 neuro-fuzzy inference system",
  journal =      "Journal of Intelligent and Fuzzy Systems",
  year =         "2012",
  volume =       "23",
  number =       "6",
  pages =        "297--304",
  keywords =     "genetic algorithms, genetic programming, Sampling
                 strategies, small and continuous data, adaptive
                 neuro-fuzzy inference system",
  DOI =          "doi:10.3233/IFS-2012-0521",
  bibdate =      "2013-01-15",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/jifs/jifs23.html#SenSGY12",
  size =         "8 pages",
  abstract =     "Sampling strategies which have very significant role
                 on examining data characteristics (i.e. imbalanced,
                 small, exhaustive) have been discussed in the
                 literature for the last couple decades. In this study,
                 the sampling problem encountered on small and
                 continuous data sets is examined. Sampling with
                 measured data by employing k-fold cross validation, and
                 sampling with synthetic data generated by fuzzy c-means
                 clustering are applied, and then the performances of
                 genetic programming (GP) and adaptive neuro fuzzy
                 inference system (ANFIS) on these data sets are
                 discussed. Concluding remarks are that when the
                 experimental results are considered, fuzzy c-means
                 based synthetic sampling is more successful than k-fold
                 cross validation while modelling small and continuous
                 data sets with ANFIS and GP, so it can be proposed for
                 these type of data sets. Additionally, ANFIS shows
                 slightly better performance than GP when synthetic data
                 is employed, but GP is less sensitive to data set and
                 produces outputs that are narrower range than ANFIS's
                 outputs while k-fold cross validation is employed.",
}

Genetic Programming entries for Sevil Sen Ebru Akcapinar Sezer Candan Gokceoglu Saffet Yagiz

Citations