| Texto completo | |
| Autor(es): |
Campello, Betania
;
Duarte, Leonardo Tomazeli
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTELLIGENT SYSTEMS, BRACIS 2024, PT III; v. 15414, p. 11-pg., 2025-01-01. |
| Resumo | |
This work proposes a novel approach to support multi-criteria decision analysis (MCDA) using tensor-based data structures and an adaptive prediction method. MCDA allows for informed decision-making involving the evaluation of different alternatives based on a set of predefined criteria. Unlike previous approaches, this methodology considers the prediction of future criteria signals rather than just consider a single-period value for the criteria. The proposed method generates a tensorial representation of the data and ranks alternatives using a MCDA method. Experimental results demonstrate that this approach outperforms existing methods, particularly in decision-making situations where future long-term consequences need to be considered. This study contributes to the development of decision support systems by providing a methodological framework that leverages the potential of signal processing and tensor-based data analysis. (AU) | |
| Processo FAPESP: | 24/03045-0 - Abordagens avançadas em decisões multicritério sob incertezas: perspectivas metodológicas e aplicadas |
| Beneficiário: | Betania Silva Carneiro Campello |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 20/09838-0 - BI0S - Brazilian Institute of Data Science |
| Beneficiário: | João Marcos Travassos Romano |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada |