Summary of Multicriteria Decision Support Employing Adaptive Prediction in a Tensor-based Feature Representation, by Betania Silva Carneiro Campello et al.
Multicriteria decision support employing adaptive prediction in a tensor-based feature representation
by Betania Silva Carneiro Campello, Leonardo Tomazeli Duarte, João Marcos Travassos Romano
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel extension of the Multicriteria Decision Analysis (MCDA) method PROMETHEE II to address the challenge of considering past data in time-varying environments. The authors use signal processing tools such as tensorial representations and adaptive prediction to structure criteria’ past data as a tensor, predict future values, and transform them into a feature domain for decision-making. Numerical experiments using real-world time series show that this approach is efficient and effective, especially for nonstationary time series. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions by considering the past when evaluating options. It’s like looking at a person’s whole history to understand them, not just their current situation. The authors use special tools from signal processing to help us do this in situations where things are changing over time. They show that their approach works well with real-world data and can be used to make more informed decisions. |
Keywords
* Artificial intelligence * Signal processing * Time series
