| Texto completo | |
| Autor(es): |
Vega-Oliveros, Didier A.
;
Nascimento, Jose
;
Lavi, Bahram
;
Rocha, Anderson
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | APPLIED SOFT COMPUTING; v. 132, p. 17-pg., 2023-01-01. |
| Resumo | |
The information on social media is vital, especially for events such as natural disasters or terrorist attacks, that might cause rapid growth of data sharing through social media networks. However, collecting and processing data of an event is a challenging task and essentially requires a great deal of data cleaning and filtering out what is relevant/irrelevant to the event. Data sifting task endeavors to identifying the related content to the depicted event data. We propose a learning strategy to dynamically learn complementary contributions from different data-driven features through a semisupervised graph-fusion technique. Our proposed method relies upon minimal training labeled data samples - ultra-small data learning. Learning through a small labeled set is also of particular interest to forensic investigators and medical researchers - concerning massive data labeling and minimizing energy-efficient computing to reduce redundancy and repetitions. We assess the effectiveness of the proposed semi-supervised method on five datasets from real-world events. Compared with prior-art (supervised and semi-supervised ones), experimental results show the proposed method achieves the best classification results and most efficient computational footprint. (c) 2022 Elsevier B.V. All rights reserved. (AU) | |
| Processo FAPESP: | 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 20/02241-9 - Reconhecimento de padrões e detecção de subeventos de destaque em dados de fontes heterogêneas |
| Beneficiário: | José Dorivaldo Nascimento Souza Júnior |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 19/26283-5 - Aprendendo pistas visuais da passagem do tempo |
| Beneficiário: | Didier Augusto Vega Oliveros |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 18/05668-3 - Coerência espaço-temporal e de características a partir de dados heterogêneos |
| Beneficiário: | Bahram Lavi Sefidgari |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |