Multicriteria decision support employing adaptive prediction in a tensor-based feature representation
This work addresses decision-making problems in dynamic environments for users of multicriteria analysis, but it is incremental as it extends an existing method.
The study tackled the challenge of incorporating past data in multicriteria decision analysis for time-varying environments by structuring criteria data as tensors and using adaptive prediction to transform predictions into a feature domain, resulting in a novel extension of PROMETHEE II that showed relevance and efficiency, especially for nonstationary time series.
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.