| Texto completo | |
| Autor(es): |
Campello, Betania Silva Carneiro
;
Duarte, Leonardo Tomazeli
;
Romano, Joao Marcos Travassos
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; v. 116, p. 11-pg., 2022-10-06. |
| Resumo | |
Multi-criteria Decision Analysis (MCDA) is a methodology that has been classically used to rank alternatives according to a set of decision criteria. The MCDA techniques have been shown to be an efficient tool in a number of real-life engineering problems. Nevertheless, most of the proposed approaches in the field do not consider the temporal characteristic of the criteria values, which can be an interesting information to be explored in order to predict future rankings. The present work proposes a novel MCDA methodology in which the past data of the criteria are considered to predict their future values. Our approach is based on a tensorial formulation, together with the use of the recursive least mean squares method in the prediction step. In addition, we consider a probabilistic prediction and use the Stochastic Multi-criteria Acceptability Analysis, which allows the decision maker to observe the degree of uncertainty in the ranking. Numerical experiments with synthetic and actual data attest to the proposal's relevance in scenarios in which the criteria values change over time. (AU) | |
| Processo FAPESP: | 20/01089-9 - Separação não-supervisionada de sinais: um estudo sobre a aplicabilidade de redes generativas adversárias e sobre modelos não-lineares baseados na Integral de Choquet |
| Beneficiário: | Leonardo Tomazeli Duarte |
| 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 |