فهرست نشریات علمی دسترسی آزاد ایران

Attitudinal Choquet integral (ACI) is a recent aggregation operator thatconsiders in the aggregation process the criteria interaction and the DM's attitude, both of which arespecific to the decision-maker. However, this capability comes at the cost of increasedcomplexity that hinders its applicability in big data analytics.To address the same, in this paper, we explore some heuristics-based forms of the ACI operator, so as to somehow overcome its complexity.We devise new and efficient forms of $\mathcal{ACI}$, and test their validityin the real world datasets, against the backdrop of preference learning.