Interval Valued

Interval-valued data analysis focuses on handling uncertainty and imprecision represented by intervals rather than single values, improving the robustness of models in various applications. Current research emphasizes extending existing frameworks like fuzzy sets, rough sets, and graph neural networks to incorporate interval data, leading to new algorithms for decision-making, information design, and posterior inference. These advancements are significant for fields dealing with inherently imprecise information, such as risk assessment, group decision-making, and machine learning on data with inherent uncertainty. The development of novel aggregation operators and interval-based information mechanisms are key areas of ongoing investigation.