A New GP-Based Wrapper Feature Construction Approach to Classification and Biomarker Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Ahmed:2014:CEC,
  title =        "A New {GP}-Based Wrapper Feature Construction Approach
                 to Classification and Biomarker Identification",
  author =       "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
  pages =        "2756--2763",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 programming, Biometrics, bioinformatics and biomedical
                 applications",
  DOI =          "doi:10.1109/CEC.2014.6900317",
  abstract =     "Mass spectrometry (MS) is a technology used for
                 identification and quantification of proteins and
                 metabolites. It helps in the discovery of proteomic or
                 metabolomic biomarkers, which aid in diseases detection
                 and drug discovery. The detection of biomarkers is
                 performed through the classification of patients from
                 healthy samples. The mass spectrometer produces high
                 dimensional data where most of the features are
                 irrelevant for classification. Therefore, feature
                 reduction is needed before the classification of MS
                 data can be done effectively. Feature construction can
                 provide a means of dimensionality reduction and aims at
                 improving the classification performance. In this
                 paper, genetic programming (GP) is used for
                 construction of multiple features. Two methods are
                 proposed for this objective. The proposed methods work
                 by wrapping a Random Forest (RF) classifier to GP to
                 ensure the quality of the constructed features.
                 Meanwhile, five other classifiers in addition to RF are
                 used to test the impact of the constructed features on
                 the performance of these classifiers. The results show
                 that the proposed GP methods improved the performance
                 of classification over using the original set of
                 features in five MS data sets.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng

Citations