Personalized individual semantics based active learning model for interactive preference elicitation to support linguistic multi-attribute decision making

Previous data-driven methods for learning the personalized individual semantics (PIS) of decision makers (DMs) in linguistic multi-attribute decision making (LMADM) often require extensive data collection, demanding excessive cognitive effort from DMs. To account for limited cognitive capacity and enhance the prediction and understanding of human assessment behavior, this paper proposes an active learning model for interactive preference elicitation. The model uses human evaluation behavior to capture PIS, thereby reflecting the underlying psychological preferences and internal cognitive states of DMs during their decision processes. This approach quantifies classification uncertainty while maintaining consistency with observed assessment behavior. To guide the active learning process, two uncertainty measures are introduced: the class distribution index and the width of the possible assignment interval. Based on these measures, four novel heuristic strategies are developed to facilitate PIS learning and improve the effectiveness of the process. Finally, a sensitivity analysis is conducted to evaluate the performance of the proposed PIS learning model.