Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification
One of the open challenges in machine learning is multi-modal data classification. A classifier model needs to be enhanced to deal with multi-modal data. This study is proposed to develop multi-codebook neural networks using intelligent Gaussian mixture model clustering for multi-modal data classifi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9915604/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850052943339847680 |
|---|---|
| author | M. Anwar Ma'Sum Noverina Alfiany Wisnu Jatmiko |
| author_facet | M. Anwar Ma'Sum Noverina Alfiany Wisnu Jatmiko |
| author_sort | M. Anwar Ma'Sum |
| collection | DOAJ |
| description | One of the open challenges in machine learning is multi-modal data classification. A classifier model needs to be enhanced to deal with multi-modal data. This study is proposed to develop multi-codebook neural networks using intelligent Gaussian mixture model clustering for multi-modal data classification. The intelligent Gaussian mixture model clustering is developed in this study prior to the development of multi-codebook models. The method analyzes the gradient of input data histogram to find the number of generated mixtures and cluster the data. The proposed multi-codebook neural network model has three variants based on the rules to find the number of clusters. The experiment result showed that the proposed three variants of multi-codebook models performed well in the synthetic and benchmark datasets. The proposed model improved the original method by 24.14%, 15.97%, and 3.71% accuracy and 0.3510, 0.0487, and 0.2031 kappa for synthetic datasets, benchmark datasets, and overall datasets respectively. By using the ANOVA test, we have proved that all three variants of the proposed multi-codebook neural network were proved to have significant improvements compared to the original version. |
| format | Article |
| id | doaj-art-49610f095c0b44e18faa72e68e27726b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-49610f095c0b44e18faa72e68e27726b2025-08-20T02:52:41ZengIEEEIEEE Access2169-35362024-01-011218944918947610.1109/ACCESS.2022.32136749915604Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data ClassificationM. Anwar Ma'Sum0https://orcid.org/0000-0002-9251-7781Noverina Alfiany1https://orcid.org/0000-0002-5990-0930Wisnu Jatmiko2https://orcid.org/0000-0002-0530-7955Faculty of Computer Science, Universitas Indonesia, Kampus UI, Depok City, Jawa Barat, IndonesiaFaculty of Computer Science, Universitas Indonesia, Kampus UI, Depok City, Jawa Barat, IndonesiaFaculty of Computer Science, Universitas Indonesia, Kampus UI, Depok City, Jawa Barat, IndonesiaOne of the open challenges in machine learning is multi-modal data classification. A classifier model needs to be enhanced to deal with multi-modal data. This study is proposed to develop multi-codebook neural networks using intelligent Gaussian mixture model clustering for multi-modal data classification. The intelligent Gaussian mixture model clustering is developed in this study prior to the development of multi-codebook models. The method analyzes the gradient of input data histogram to find the number of generated mixtures and cluster the data. The proposed multi-codebook neural network model has three variants based on the rules to find the number of clusters. The experiment result showed that the proposed three variants of multi-codebook models performed well in the synthetic and benchmark datasets. The proposed model improved the original method by 24.14%, 15.97%, and 3.71% accuracy and 0.3510, 0.0487, and 0.2031 kappa for synthetic datasets, benchmark datasets, and overall datasets respectively. By using the ANOVA test, we have proved that all three variants of the proposed multi-codebook neural network were proved to have significant improvements compared to the original version.https://ieeexplore.ieee.org/document/9915604/Multi-modalclassificationmulti-codebookneural networksintelligent clusteringGaussian mixture model |
| spellingShingle | M. Anwar Ma'Sum Noverina Alfiany Wisnu Jatmiko Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification IEEE Access Multi-modal classification multi-codebook neural networks intelligent clustering Gaussian mixture model |
| title | Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification |
| title_full | Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification |
| title_fullStr | Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification |
| title_full_unstemmed | Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification |
| title_short | Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification |
| title_sort | generating multi codebook neural network by using intelligent gaussian mixture model clustering based on histogram information for multi modal data classification |
| topic | Multi-modal classification multi-codebook neural networks intelligent clustering Gaussian mixture model |
| url | https://ieeexplore.ieee.org/document/9915604/ |
| work_keys_str_mv | AT manwarmasum generatingmulticodebookneuralnetworkbyusingintelligentgaussianmixturemodelclusteringbasedonhistograminformationformultimodaldataclassification AT noverinaalfiany generatingmulticodebookneuralnetworkbyusingintelligentgaussianmixturemodelclusteringbasedonhistograminformationformultimodaldataclassification AT wisnujatmiko generatingmulticodebookneuralnetworkbyusingintelligentgaussianmixturemodelclusteringbasedonhistograminformationformultimodaldataclassification |