Thalassaemia classification by neural networks and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Thalassaemia classification by neural networks and
                 genetic programming",
  author =       "Waranyu Wongseree and Nachol Chaiyaratana and 
                 Kanjana Vichittumaros and Pranee Winichagoon and 
                 Suthat Fucharoen",
  journal =      "Information Sciences",
  year =         "2007",
  number =       "3",
  volume =       "177",
  pages =        "771--786",
  month =        feb,
  bibdate =      "2006-12-21",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2006.07.009",
  abstract =     "This paper presents the use of a neural network and a
                 decision tree, which is evolved by genetic programming
                 (GP), in thalassaemia classification. The aim is to
                 differentiate between thalassaemic patients, persons
                 with thalassaemia trait and normal subjects by
                 inspecting characteristics of red blood cells,
                 reticulocytes and platelets. A structured
                 representation on genetic algorithms for non-linear
                 function fitting or STROGANOFF is the chosen
                 architecture for genetic programming implementation.
                 For comparison, multilayer perceptrons are explored in
                 classification via a neural network. The classification
                 results indicate that the performance of the GP-based
                 decision tree is approximately equal to that of the
                 multilayer perceptron with one hidden layer. But the
                 multilayer perceptron with two hidden layers, which is
                 proven to have the most suitable architecture among
                 networks with different number of hidden layers,
                 outperforms the GP-based decision tree. Nonetheless,
                 the structure of the decision tree reveals that some
                 input features have no effects on the classification
                 performance. The results confirm that the
                 classification accuracy of the multilayer perceptron
                 with two hidden layers can still be maintained after
                 the removal of the redundant input features. Detailed
                 analysis of the classification errors of the multilayer
                 perceptron with two hidden layers, in which a reduced
                 feature set is used as the network input, is also
                 included. The analysis reveals that the classification
                 ambiguity and misclassification among persons with
                 minor thalassaemia trait and normal subjects is the
                 main cause of classification errors. These results
                 suggest that a combination of a multilayer perceptron
                 with a blood cell analysis may give rise to a
                 guideline/hint for further investigation of
                 thalassaemia classification.",
  notes =        "a Research and Development Centre for Intelligent
                 Systems, Department of Electrical Engineering, Faculty
                 of Engineering, King Mongkuts Institute of Technology
                 North Bangkok, 1518 Piboolsongkram Road, Bangsue,
                 Bangkok 10800, Thailand

                 b Thalassaemia Research Centre, Institute of Science
                 and Technology for Research and Development, Mahidol
                 University, Nakhonpathom 73170, Thailand",

Genetic Programming entries for Waranyu Wongseree Nachol Chaiyaratana Kanjana Vichittumaros Pranee Winichagoon Suthat Fucharoen