Catalog Home Page

Comparing the performance of different neural networks for binary classification problems

Jeatrakul, P. and Wong, K.W. (2009) Comparing the performance of different neural networks for binary classification problems. In: 8th International Symposium on Natural Language Processing, SNLP '09, 20 - 22 Oct, Bangkok Thailand pp. 111-115.

PDF - Published Version
Download (229kB)
    Link to Published Version:
    *Subscription may be required


    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.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © IEEE
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Item Control Page


    Downloads per month over past year