Integrating Tensor-Based Data Analytics and Adaptive Prediction for Informed Decision-Making Support

Autor(es)

Campello, Betania ; Duarte, Leonardo Tomazeli

• 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 em Engenharia