Linear machine weight adaptation in a genetic programming classifier that classifies medical data

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

@InProceedings{Pakri:2008:ICCCE,
  author =       "Noor Azilah Pakri and Abdul Razak Hussain and 
                 Khairul Azhar Kasmiran",
  title =        "Linear machine weight adaptation in a genetic
                 programming classifier that classifies medical data",
  booktitle =    "International Conference on Computer and Communication
                 Engineering, ICCCE 2008",
  year =         "2008",
  month =        may,
  pages =        "236--240",
  keywords =     "genetic algorithms, genetic programming, decision tree
                 classifier, error elimination, fitness evaluation,
                 genetic programming classifier, input patterns, linear
                 machine decision tree, linear machine weight
                 adaptation, medical data classification,
                 misclassification problem, oblique decision tree
                 induction, robust GP fitness function, tree
                 construction, data analysis, decision trees, medical
                 computing, pattern classification",
  DOI =          "doi:10.1109/ICCCE.2008.4580603",
  abstract =     "While there has been a significant improvement in the
                 overall decision tree classifier performance, not many
                 methods focuses on the explicit treatment or
                 measurement of sensitivity and specificity. Present
                 methods generally pay less attention to the existence
                 of misclassified input patterns and often fail to
                 address the correction needed for error elimination or
                 adjustment. This paper addresses the handling of the
                 misclassification problem with the long term goal of
                 improving the classifier accuracy in terms of
                 sensitivity and specificity. The technique proposed is
                 an oblique decision tree induction approach that relies
                 on genetic programming (GP) and incorporates the linear
                 machine decision tree algorithm through fitness
                 evaluation. A robust GP fitness function handles
                 generality and noise through weight adaptation during
                 tree construction. By involving error correction each
                 time the classifier is constructed, the proposed
                 approach increases the classifier accuracy not only in
                 terms of sensitivity but also specificity. The
                 comparative evaluation of the proposed approach with
                 selected classifier methods is presented in terms of
                 accuracy, simplicity (size) and the construction time
                 of the tree.",
  notes =        "Also known as \cite{4580603}",
}

Genetic Programming entries for Noor Azilah Pakri Abdul Razak Hussain Khairul Azhar Kasmiran

Citations