Clustering versus incremental learning multi-codebook fuzzy neural network for multi-modal data classification

Muhammad Anwar Ma'sum, Hadaiq Rolis Sanabila, Petrus Mursanto, Wisnu Jatmiko

Research output: Contribution to journalArticle

Abstract

One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.

Original languageEnglish
Article number6
JournalComputation
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • Clustering
  • Fuzzy
  • Incremental learning
  • Multi-codebook
  • Multi-modal
  • Neural network

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