A Clustering-Based Multi-Layer Distributed Ensemble for Neurological Diagnostics in Cloud Services
| Autor: | Andrei V. Kelarev, Morshed U. Chowdhury, Jemal H. Abawajy, Herbert F. Jelinek |
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| Rok vydání: | 2020 |
| Předmět: |
Distributed database
Computer Networks and Communications business.industry Computer science Cloud computing Cardiac autonomic neuropathy Base (topology) Machine learning computer.software_genre Computer Science Applications Data modeling Hardware and Architecture Data mining Artificial intelligence business Cluster analysis Multi layer computer Software Information Systems Data transmission |
| Zdroj: | IEEE Transactions on Cloud Computing. 8:473-483 |
| ISSN: | 2372-0018 |
| DOI: | 10.1109/tcc.2016.2567389 |
| Popis: | This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks. |
| Databáze: | OpenAIRE |
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