Multi codebook LVQ-based artificial neural network using clustering approach

M. Anwar Ma'Sum, H. R. Sanabila, Wisnu Jatmiko, Aprinaldi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In this paper we proposed multicodebook LVQ-based artificial neural network classifier using clustering approach. The classifiers are LVQ, LVQ2-1, GLVQ, and FNGLVQ. The clustering algorithm used to build multi codebook is K-Means, IK-Means, and GMM. Experiment result shows that on synthteic dataset multi codebook FNGLVQ using GMM clustering has higest improvement with 19,53% mprovement compared to FNGLVQ. Whereas on bencmark dataset multi codebook LVQ2-1 using K-Means clustering has higest improvement with 5,83% improvement cmpared to LVQ-2.1.

Original languageEnglish
Title of host publicationICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-268
Number of pages6
ISBN (Electronic)9781509003624
DOIs
Publication statusPublished - 19 Feb 2016
EventInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015 - Depok, Indonesia
Duration: 10 Oct 201511 Oct 2015

Publication series

NameICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

ConferenceInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
CountryIndonesia
CityDepok
Period10/10/1511/10/15

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  • Cite this

    Ma'Sum, M. A., Sanabila, H. R., Jatmiko, W., & Aprinaldi (2016). Multi codebook LVQ-based artificial neural network using clustering approach. In ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings (pp. 263-268). [7415193] (ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACSIS.2015.7415193