Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification

M. Anwar Ma'Sum, Wisnu Jatmiko

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

1 Citation (Scopus)

Abstract

One of the challenge in classification is classification in multimodal data. This paper proposed multi-codebook fuzzy neural network by using incremental learning for multimodal data classification. There are 2 variations of the proposed method, one uses a static threshold, and the other uses a dynamic threshold. Based on the experiment result, the multicodebook FNGLVQ using dynamic incremental learning has the highest improvement compared to the original FNGLVQ. It achieves 15.65% margin in synthetic dataset, 5.02 % margin in benchmark dataset, and 11.30% on average all dataset.

Original languageEnglish
Title of host publication2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages205-210
Number of pages6
ISBN (Electronic)9781728122298
DOIs
Publication statusPublished - Jul 2019
Event4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 - Nagoya, Japan
Duration: 13 Jul 201915 Jul 2019

Publication series

Name2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019

Conference

Conference4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019
CountryJapan
CityNagoya
Period13/07/1915/07/19

Keywords

  • classification
  • fuzzy
  • multi-codebook
  • multimodal
  • neural network

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    Ma'Sum, M. A., & Jatmiko, W. (2019). Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification. In 2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 (pp. 205-210). [8935971] (2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIRS.2019.8935971