Directly Constructing Multiple Features for Classification with Missing Data using Genetic Programming with Interval Functions

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

  author =       "Cao Truong Tran and Mengjie Zhang and 
                 Peter Andreae and Bing Xue",
  title =        "Directly Constructing Multiple Features for
                 Classification with Missing Data using Genetic
                 Programming with Interval Functions",
  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 =        "69--70",
  keywords =     "genetic algorithms, genetic programming: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2909002",
  abstract =     "Missing values are a common issue in many industrial
                 and real-world datasets. Genetic programming-based
                 multiple feature construction (GPMFC) is a recent
                 promising filter approach to constructing multiple
                 features for classification using genetic programming
                 (GP). GPMFC has been demonstrated to improve
                 classification performance and reduce the complexity of
                 many decision trees and rule-based classifiers, but it
                 cannot work with missing data. To deal with missing
                 data, this paper propose IGPMFC, an extension of GPMFC
                 that use interval functions as the GP function set to
                 directly construct multiple features for classification
                 with missing data. Empirical results on five datasets
                 and four classifiers show that IGPMFC can substantially
                 improve the performance and reduce the complexity of
                 the classifiers when faced with missing data.",
  notes =        "Distributed at GECCO-2016.",

Genetic Programming entries for Cao Truong Tran Mengjie Zhang Peter Andreae Bing Xue