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Automatic Disease Vector Mosquitoes Identification via Hierarchical Data Stream Classification by Eduardo Tieppo; Jean Paul Barddal; Júlio Cesar Nievola is a scholarly article available to read on EtoBox.

What is Automatic Disease Vector Mosquitoes Identification via Hierarchical Data Stream Classification about?

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 b

Author

Eduardo Tieppo; Jean Paul Barddal; Júlio Cesar Nievola

Publisher

ACM

Published

2022

Language

EN