Name
Title | ||
|---|---|---|
Clustering Versus Incremental Learning Multi-Codebook Fuzzy Neural Network For Multi-Modal Data Classification |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/computation8010006 | COMPUTATION |
Keywords | DocType | Volume |
neural network, fuzzy, multi-codebook, multi-modal, clustering, incremental learning | Journal | 8 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Anwar Ma'sum | 1 | 0 | 0.34 |
| hadaiq sanabila | 2 | 1 | 1.06 |
| Petrus Mursanto | 3 | 0 | 0.34 |
| W. Jatmiko | 4 | 105 | 23.09 |