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Automated feature weighting in fuzzy declustering-based vector quantization
The University of New South Wales, ADFA, Canberra, Australia.
The University of New South Wales, ADFA, Canberra, Australia.ORCID-id: 0000-0002-4255-5130
CSIRO Mathematics, Informatics and Statistics, Locked Bag 17, N. Ryde, Australia.
2010 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Resurstyp
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.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2010. s. 686-689
Serie
Pattern Recognition (ICPR), 2010 20th International Conference on, ISSN 1051-4651
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-127900DOI: 10.1109/ICPR.2010.173Scopus ID: 2-s2.0-78149483865ISBN: 978-1-4244-7542-1 (tryckt)OAI: oai:DiVA.org:liu-127900DiVA, id: diva2:928902
Konferanse
2010 International Conference on Pattern Recognition. 23-26 Aug. 2010 Istanbul
Tilgjengelig fra: 2016-05-17 Laget: 2016-05-13 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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