Genetic Programming Models for Classification of Data from Biological Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{Raghuraj:2007:cec,
  author =       "K. Rao Raghuraj and S. Lakshminarayanan and Kyaw Tun",
  title =        "Genetic Programming Models for Classification of Data
                 from Biological Systems",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "4154--4161",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, biological
                 systems, data classification, data driven evolutionary
                 modeling approach, genetic programming models,
                 illustrative datasets, linear classifiers, nonlinear
                 classifiers, nonlinear interactions, biology computing,
                 data handling",
  DOI =          "doi:10.1109/CEC.2007.4425013",
  ISBN =         "1-4244-1340-0",
  file =         "1827.pdf",
  abstract =     "Data classification problems especially for biological
                 systems pose serious challenges mainly due to the
                 presence of multivariate and highly nonlinear
                 interactions between variables. Specimens that need to
                 be distinguished from one another show similar profiles
                 and cannot be separated easily based on decision
                 boundaries or available discriminating rules.
                 Alternatively, inter-relations among the feature
                 vectors can be exploited for distinguishing samples
                 into specific classes. Such variable interaction models
                 are difficult to establish given the nature of
                 biological systems. Genetic Programming, a data driven
                 evolutionary modelling approach is proposed here to be
                 a potential tool for designing variable dependency
                 models and exploiting them further for class
                 discrimination. A new and alternative GP model based
                 classification approach is proposed. Analysis is
                 carried out using illustrative datasets and the
                 performance is bench marked against well established
                 linear and nonlinear classifiers like LDA, kNN, CART,
                 ANN and SVM. It is demonstrated that GP based models
                 can be effective tools for separating unknown
                 biological systems into different classes. The new
                 classification method has the potential to be
                 effectively and successfully extended to many systems
                 biology applications of recent interest.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for K Rao Raghuraj S Lakshminarayanan Kyaw Tun

Citations