Cited By
View all- Tieppo ENievola JBarddal J(2024)Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilitiesApplied Soft Computing10.1016/j.asoc.2024.111271152(111271)Online publication date: Feb-2024
Automatic disease vector mosquitoes identification via hierarchical data stream classification
Pages 1005 - 1012
Abstract
Vector-borne diseases (VBDs), such as Dengue or Malaria, are one of the main concerns of public health agencies and governments. These diseases are mainly spread by mosquitoes acting as vectors by transmitting infected blood between humans. Machine learning can be used to design and improve control strategies of VBDs by providing models able to recognize disease vector mosquitoes and automatically capture or kill harmful species. The automatic identification of disease vector mosquitoes was not yet addressed concerning the hierarchical classification of data streams. Thus, reliable information has not been used to improve learning models, such as mosquitoes' hierarchical taxonomy. In this study, we propose a framework for the automatic identification of disease vector mosquitoes in the context of the hierarchical classification of data streams area. To this end, we propose a hierarchical adaptation of a disease vector mosquitoes' dataset to include their taxonomy and introduce kNC and Dribble, two novel classification methods fitted to hierarchical data streams representing the mosquitoes. Results depicted that our framework, using summarization techniques, achieves significantly better prediction and processing speed rates when compared to existing state-of-the-art models.
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Index Terms
- Automatic disease vector mosquitoes identification via hierarchical data stream classification
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April 2022
2099 pages
ISBN:9781450387132
DOI:10.1145/3477314
- Conference Chairs:
- Jiman Hong,
- Miroslav Bures,
- Program Chairs:
- Juw Won Park,
- Tomas Cerny
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Published: 06 May 2022
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View all- Tieppo ENievola JBarddal J(2024)Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilitiesApplied Soft Computing10.1016/j.asoc.2024.111271152(111271)Online publication date: Feb-2024
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