Thalassaemia classification by neural networks and genetic programming
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- @Article{journals/isci/WongsereeCVWF07,
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title = "Thalassaemia classification by neural networks and
genetic programming",
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author = "Waranyu Wongseree and Nachol Chaiyaratana and
Kanjana Vichittumaros and Pranee Winichagoon and
Suthat Fucharoen",
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journal = "Information Sciences",
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year = "2007",
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number = "3",
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volume = "177",
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pages = "771--786",
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month = feb,
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bibdate = "2006-12-21",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/isci/isci177.html#WongsereeCVWF07",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2006.07.009",
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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.",
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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
Citations