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Automated feature weighting in fuzzy declustering-based vector quantization
The University of New South Wales, ADFA, Canberra, ACT 2600, Australia.
The University of New South Wales, ADFA, Canberra, ACT 2600, Australia.ORCID iD: 0000-0002-4255-5130
CSIRO Mathematics, Informatics and Statistics, Locked Bag 17, N. Ryde, NSW 1670, Australia.
2010 (English)Conference paper (Refereed)Text
Abstract [en]

Feature weighting plays an important role in improving the performance of clustering technique. We propose an automated feature weighting in fuzzy declustering-based vector quantization (FDVQ), namely AFDVQ algorithm, for enhancing effectiveness and efficiency in classification. The proposed AFDVQ imposes weights on the modified fuzzy c-means (FCM) so that it can automatically calculate feature weights based on their degrees of importance rather than treating them equally. Moreover, the extension of FDVQ and AFDVQ algorithms based on generalized improved fuzzy partitions (GIFP), known as GIFP-FDVQ and GIFP-AFDVQ respectively, are proposed. The experimental results on real data (original and noisy data) and modified data (biased and noisy-biased data) have demonstrated that the proposed algorithms outperformed standard algorithms in classifying clusters especially for biased data.

Place, publisher, year, edition, pages
2010. 686-689 p.
Series
, Pattern Recognition (ICPR), 2010 20th International Conference on, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127900DOI: 10.1109/ICPR.2010.173ISBN: 978-1-4244-7542-1OAI: oai:DiVA.org:liu-127900DiVA: diva2:928902
Conference
2010 International Conference on Pattern Recognition. 23-26 Aug. 2010 Istanbul
Available from: 2016-05-17 Created: 2016-05-13 Last updated: 2016-06-02

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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ReferencesLink to record
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