Multicriteria decision support employing adaptive prediction in a tensor-based feature representation

• URL: http://arxiv.org/abs/2401.06868v1
• Date: Fri, 12 Jan 2024 19:46:29 GMT
• Title: Multicriteria decision support employing adaptive prediction in a tensor-based feature representation
• Authors: Betania Silva Carneiro Campello, Leonardo Tomazeli Duarte, Jo\~ao Marcos Travassos Romano
• Abstract summary: Multicriteria decision analysis is a widely used tool to support decisions in which a set of alternatives should be ranked or classified. Recent studies have shown the relevance of considering not only current evaluations of each criterion but also past data. This study deals with this challenge via essential tools of signal processing, such as tensorial representations and adaptive prediction.
• Score: 8.333246626497361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multicriteria decision analysis (MCDA) is a widely used tool to support decisions in which a set of alternatives should be ranked or classified based on multiple criteria. Recent studies in MCDA have shown the relevance of considering not only current evaluations of each criterion but also past data. Past-data-based approaches carry new challenges, especially in time-varying environments. This study deals with this challenge via essential tools of signal processing, such as tensorial representations and adaptive prediction. More specifically, we structure the criteria' past data as a tensor and, by applying adaptive prediction, we compose signals with these prediction values of the criteria. Besides, we transform the prediction in the time domain into a most favorable decision making domain, called the feature domain. We present a novel extension of the MCDA method PROMETHEE II, aimed at addressing the tensor in the feature domain to obtain a ranking of alternatives. Numerical experiments were performed using real-world time series, and our approach is compared with other existing strategies. The results highlight the relevance and efficiency of our proposal, especially for nonstationary time series.

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