Collaborative Fuzzy Linguistic Learning to Low-Resource and Robust Decision System Based on Bounded Rationality

Low-resource languages are challenging to process intelligent decision systems due limited data and resources. As an effective way of processing low-resource in systems, fuzzy linguistic approaches excel transforming original uncertain information into highly structured learning valid rules between complex structures. However, existing methods may not fully capture realistic features multi-attribute group decision-making (MAGDM), such as incomplete hesitant expressions, stable fusion, bounded rationality decision-makers (DMs). Therefore, it is necessary develop a collaborative language system based on rationality, robust decision-making. Specifically, we present new multi-granularity (MG) (GDM) scheme by using MULTIMOORA (Multi-Objective Optimization Ratio Analysis plus the full MULTIplicative form) PT (Prospect Theory) for (I-HFL-ISs), where MG GDM aims discover knowledge from MAGDM problems with features. To achieve above goal, first introduce concept MG-I-HFL-ISs represent incomplete, imprecise evaluation offered multiple Then, apply transformation convert MG-HFL-ISs, use probability rough set (PRS) series MG-HFL-PRSs support MULTIMOORA. Afterwards, HFL method designed integrating solving MG-I-HFL-ISs. The proposed can effectively synthesize mine useful knowledge. At last, drug selection case simulated performed showing scheme.