Detecting Bacterial Vaginosis Using Machine Learning

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

  author =       "Yolanda S. Baker and Rajeev Agrawal and 
                 James A. Foster and Daniel Beck and Gerry Dozier",
  title =        "Detecting Bacterial Vaginosis Using Machine Learning",
  booktitle =    "Proceedings of the 2014 ACM Southeast Regional
  year =         "2014",
  pages =        "46:1--46:4",
  address =      "Kennesaw, Georgia, USA",
  month =        mar # " 28-29",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming",
  acmid =        "2638521",
  isbn13 =       "978-1-4503-2923-1",
  DOI =          "doi:10.1145/2638404.2638521",
  size =         "4 pages",
  abstract =     "Bacterial Vaginosis (BV) is the most common of vaginal
                 infections diagnosed among women during the years where
                 they can bear children. Yet, there is very little
                 insight as to how it occurs. There are a vast number of
                 criteria that can be taken into consideration to
                 determine the presence of BV. The purpose of this paper
                 is two-fold; first to discover the most significant
                 features necessary to diagnose the infection, second is
                 to apply various classification algorithms on the
                 selected features. It is observed that certain feature
                 selection algorithms provide only a few features;
                 however, the classification results are as good as
                 using a large number of features.",
  notes =        "ACM SE '14",

Genetic Programming entries for Yolanda S Baker Rajeev Agrawal James A Foster Daniel Beck Gerry Dozier