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Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification
- Source :
- Computation, Vol 8, Iss 1, p 6 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
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.
Details
- Language :
- English
- ISSN :
- 20793197
- Volume :
- 8
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Computation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.72f86f063cac4ef0bb38e0b3ba7a866e
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/computation8010006