Improving Logistic Regression Classification of Credit Approval with Features Constructed by Kaizen Programming

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@InProceedings{deMelo:2016:GECCOcomp,
  author =       "Vinicius Veloso {de Melo} and Wolfgang Banzhaf",
  title =        "Improving Logistic Regression Classification of Credit
                 Approval with Features Constructed by Kaizen
                 Programming",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "61--62",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming: Poster",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2908963",
  abstract =     "we employ the recently proposed Kaizen Programming
                 (KP) approach to find high-quality nonlinear
                 combinations of the original features in a dataset. KP
                 constructs many complementary features at the same
                 time, which are selected by their importance, not by
                 model quality. We investigated our approach in a
                 well-known real-world credit scoring dataset. When
                 compared to related approaches, KP reaches similar or
                 better results, but evaluates fewer models.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Vinicius Veloso de Melo Wolfgang Banzhaf

Citations