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Data cleaning using complementary fuzzy support vector machine technique

Pruengkarn, R., Wong, K.W. and Fung, C.C. (2016) Data cleaning using complementary fuzzy support vector machine technique. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M. and Liu, D., (eds.) Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part II. Springer International Publishing, pp. 160-167.

Link to Published Version: http://dx.doi.org/10.1007/978-3-319-46672-9_19
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Abstract

n this paper, a Complementary Fuzzy Support Vector Machine (CMTFSVM) technique is proposed to handle outlier and noise in classification problems. Fuzzy membership values are applied for each input point to reflect the degree of importance of the instances. Datasets from the UCI and KEEL are used for the comparison. In order to confirm the proposed methodology, 40 % random noise is added to the datasets. The experiment results of CMTFSVM are analysed and compared with the Complementary Neural Network (CMTNN). The outcome indicated that the combined CMTFSVM outperformed the CMTNN approach.

Publication Type: Book Chapter
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer International Publishing
Copyright: Springer International Publishing AG
Other Information: Series Title: Lecture Notes in Computer Science; Vol. No. 9948; ISSN: 0302-9743
URI: http://researchrepository.murdoch.edu.au/id/eprint/34635
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