دانلود مقاله ISI انگلیسی شماره 79490
عنوان فارسی مقاله

طبقه بندی تالاسمی توسط شبکه های عصبی و برنامه نویسی ژنتیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79490 2007 16 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Thalassaemia classification by neural networks and genetic programming
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 177, Issue 3, 1 February 2007, Pages 771–786

کلمات کلیدی
طبقه بندی تالاسمی؛ شبکه عصبی؛ برنامه نویسی ژنتیک
پیش نمایش مقاله
پیش نمایش مقاله طبقه بندی تالاسمی توسط شبکه های عصبی و برنامه نویسی ژنتیک

چکیده انگلیسی

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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