Breast Cancer Detection with Logistic Regression improved by features constructed by Kaizen Programming in a hybrid approach

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@InProceedings{deMelo:2016:CEC,
  author =       "Vinicius Veloso {de Melo}",
  title =        "Breast Cancer Detection with Logistic Regression
                 improved by features constructed by Kaizen Programming
                 in a hybrid approach",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "16--23",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743773",
  abstract =     "Breast cancer is known as the second largest cause of
                 cancer deaths among women, but thankfully it can be
                 cured if diagnosed early. There have been many
                 investigations on methods to improve the accuracy of
                 the diagnostic, and Machine Learning (ML) and
                 Evolutionary Computation (EC) tools are among the most
                 successfully employed modern methods. On the other
                 hand, Logistic Regression (LR), a traditional and
                 popular statistical method for classification, is not
                 commonly used by computer scientists as those modern
                 methods usually outperform it. Here we show that LR can
                 achieve results that are similar to those of ML and EC
                 methods and can even outperform them when useful
                 knowledge is discovered in the dataset. In this paper,
                 we employ the recently proposed Kaizen Programming (KP)
                 approach with LR to construct high-quality nonlinear
                 combinations of the original features resulting in new
                 sets of features. Experimental analysis indicates that
                 the new sets provide significantly better predictive
                 accuracy than the original ones. When compared to
                 related work from the literature, it is shown that the
                 proposed approach is competitive and a promising method
                 for automatic feature construction.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Vinicius Veloso de Melo

Citations