Improving the Accuracy of Cancer Prediction by Ensemble Confidence Evaluation

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  author =       "Michael Affenzeller and Stephan M. Winkler and 
                 Herbert Stekel and Stefan Forstenlechner and Stefan Wagner",
  title =        "Improving the Accuracy of Cancer Prediction by
                 Ensemble Confidence Evaluation",
  booktitle =    "Computer Aided Systems Theory - EUROCAST 2013",
  year =         "2013",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "8111",
  series =       "Lecture Notes in Computer Science",
  pages =        "316--323",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb # " 10-15",
  publisher =    "Springer",
  note =         "Revised Selected Papers, Part I",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-53855-1",
  language =     "English",
  URL =          "",
  DOI =          "doi:10.1007/978-3-642-53856-8_40",
  size =         "8 pages",
  abstract =     "This paper discusses a novel approach for the
                 prediction of breast cancer, melanoma and cancer in the
                 respiratory system using ensemble modelling techniques.
                 For each type of cancer, a set of unequally complex
                 predictors are learnt by symbolic classification based
                 on genetic programming. In addition to standard
                 ensemble modeling, where the prediction is based on a
                 majority voting of the prediction models, two
                 confidence parameters are used which aim to quantify
                 the trustworthiness of each single prediction based on
                 the clearness of the majority voting. Based on the
                 calculated confidence of each ensemble prediction,
                 predictions might be considered uncertain. The
                 experimental part of this paper discusses the increase
                 of accuracy that can be obtained for those samples
                 which are considered trustable depending on the ratio
                 of predictions that are considered trustable.",

Genetic Programming entries for Michael Affenzeller Stephan M Winkler Herbert Stekel Stefan Forstenlechner Stefan Wagner