Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.2610

@Article{MorenoTorres2010,
  author =       "Jose G. Moreno-Torres and Xavier Llora and 
                 David E. Goldberg and Rohit Bhargava",
  title =        "Repairing fractures between data using genetic
                 programming-based feature extraction: {A} case study in
                 cancer diagnosis",
  journal =      "Information Sciences",
  year =         "2013",
  volume =       "222",
  pages =        "805--823",
  ISSN =         "0020-0255",
  doi =          "doi:10.1016/j.ins.2010.09.018",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-515SRJV-1/2/ba19f0969d5d756d2abd12ac6f843d9f",
  keywords =     "genetic algorithms, genetic programming, Feature
                 extraction, Fractures between data, Biological data,
                 Cancer diagnosis, Different laboratories",
  size =         "19 pages",
  abstract =     "There is an underlying assumption on most model
                 building processes: given a learnt classifier, it
                 should be usable to explain unseen data from the same
                 given problem. Despite this seemingly reasonable
                 assumption, when dealing with biological data it tends
                 to fail; where classifiers built out of data generated
                 using the same protocols in two different laboratories
                 can lead to two different, non-interchangeable,
                 classifiers. There are usually too many uncontrollable
                 variables in the process of generating data in the lab
                 and biological variations, and small differences can
                 lead to very different data distributions, with a
                 fracture between data. This paper presents a
                 genetics-based machine learning approach that performs
                 feature extraction on data from a lab to help increase
                 the classification performance of an existing
                 classifier that was built using the data from a
                 different laboratory which uses the same protocols,
                 while learning about the shape of the fractures between
                 data that motivated the bad behaviour.

                 The experimental analysis over benchmark problems
                 together with a real-world problem on prostate cancer
                 diagnosis show the good behavior of the proposed
                 algorithm.",
  notes =        "Including Special Section on New Trends in Ambient
                 Intelligence and Bio-inspired Systems",
}

Genetic Programming entries for Jose Garcia Moreno-Torres Xavier Llora David E Goldberg Rohit Bhargava