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Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems

Kraipeerapun, P. and Fung, C.C. (2008) Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems. In: 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, IEEE-DEST 2008, 26-29 Feb. 2008, Phitsanulok, Thailand pp. 34-37.

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Link to Published Version: http://dx.doi.org/10.1109/DEST.2008.4635138
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Abstract

In this paper, the classification results obtained from several kinds of support vector machines (SVM) and neural networks (NN) are compared with our proposed classifier. Our approach is based on neural networks and interval neutrosophic sets which are used to classify the input patterns into one of the two binary class outputs. The comparison is based on several classical benchmark problems from UCI machine learning repository. We have found that the performance of our approaches are comparable to the existing classifiers. However, our approach has taken into account of the uncertainty in the classification process.

Publication Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: IEEE
Copyright: (c) 2008 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.
URI: http://researchrepository.murdoch.edu.au/id/eprint/581
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