Comparing the performance of different neural networks for binary classification problems

P. Jeatrakul; K.W. Wong

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Comparing the performance of different neural networks for binary classification problems

Conference paper

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Comparing the performance of different neural networks for binary classification problems

P. Jeatrakul and K.W. Wong

IEEE

8th International Symposium on Natural Language Processing, SNLP '09 (Bangkok Thailand, 20/10/2009–22/10/2009)

2009

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Abstract

Classification problem is a decision making task where many researchers have been working on. There are a number of techniques proposed to perform classification. Neural network is one of the artificial intelligent techniques that has many successful examples when applying to this problem. This paper presents a comparison of neural network techniques for binary classification problems. The classification performance obtained by five different types of neural networks for comparison are Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), and Complementary Neural Network (CMTNN). The comparison is done based on three benchmark data sets obtained from UCI machine learning repository. The results show that CMTNN typically provide better classification results when comparing to techniques applied to binary classification problems.

Details

Title: Comparing the performance of different neural networks for binary classification problems
Authors/Creators: P. Jeatrakul (Author/Creator)
K.W. Wong (Author/Creator)
Conference: 8th International Symposium on Natural Language Processing, SNLP '09 (Bangkok Thailand, 20/10/2009–22/10/2009)
Publisher: IEEE
Identifiers: 991005543162207891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Conference paper
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