Genetic Programming Based Feature Construction for Classification with Incomplete Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Tran:2017:GECCOa,
  author =       "Cao Truong Tran and Mengjie Zhang and 
                 Peter Andreae and Bing Xue",
  title =        "Genetic Programming Based Feature Construction for
                 Classification with Incomplete Data",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1033--1040",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071183",
  DOI =          "doi:10.1145/3071178.3071183",
  acmid =        "3071183",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming,
                 classification, feature construction, incomplete data",
  month =        "15-19 " # jul,
  abstract =     "Missing values are an unavoidable problem in many
                 real-world datasets. Dealing with incomplete data is an
                 crucial requirement for classification because
                 inadequate treatment of missing values often causes
                 large classification error. Feature construction has
                 been successfully applied to improve classification
                 with complete data, but it has been seldom applied to
                 incomplete data. Genetic programming-based multiple
                 feature construction (GPMFC) is a current encouraging
                 feature construction method which uses genetic
                 programming to evolve new multiple features from
                 original features for classification tasks. GPMFC can
                 improve the accuracy and reduce the complexity of many
                 decision trees and rule-based classifiers; however, it
                 cannot directly work with incomplete data. This paper
                 proposes IGPMFC which is extended from GPMFC to tackle
                 with incomplete data. IGPMFC uses genetic programming
                 with interval functions to directly evolve multiple
                 features for classification with incomplete data.
                 Experimental results reveal that not only IGPMFC can
                 substantially improve the accuracy, but also can reduce
                 the complexity of learnt classifiers facing with
                 incomplete data.",
  notes =        "Also known as \cite{Tran:2017:GPB:3071178.3071183}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

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

Citations