Comparison of neural network and decision tree induction in classification of radar returns from the ionosphere

• Autorzy: Salankar S. S. , Patre B. M.
• Języki publikacji: EN

Abstrakty

EN

This study compares the performance of decision tree (CART) and two neural network configurations, namely a well-known Multilayer Perceptron Neural Network (MLP NN) and the Radial Basis Function Neural Network (RBF NN) on the radar ionosphere databases. The task is to discriminate between radar returns from the ionosphere into "good returns" (evidence of structure) and "bad returns". It is shown that the proposed RBF neural network based classifier, consistently, has 100% accuracy on "bad" instances and 99.18% accuracy on "good" instances of testing data sets. The results prove that the proposed RBF NN classifier clearly outperforms the MLP NN and Decision Tree based classifiers on the testing datasets even after attempting different data partitions.

Słowa kluczowe

EN

classification; multilayer perceptron; decision tree; receiver operating characteristics

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2009
• Tom: Vol. 35, no 4
• Strony: 61--67
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Salankar S. S.

autor

Patre B. M.

• Department of Electronics and Telecommunication, B.D. College of Engineering, 442102 Sevagram, India,

Bibliografia

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