Weight assignment of attribute is considered as a key part of multiple attribute decision making (MADM), and this is also applicable to labeled multiple attribute decision making (LMADM) that is a decision theory specially proposed for the dataset with labels. However, regarding the decision making of massive data characterized by redundancy and uncertainty, more means including attribute selection and uncertainty processing should be considered to solve these problems. Based on the traditional framework of LMADM, this paper deduces a new framework to adapt to the decision making of massive data. With respect to the uncertainty generated from data and decision process, a fuzzy neighborhood three-way decision model (FN3WD) is proposed, in which the fuzzy neighborhood relationship can address the uncertainty of data and the three-way decision theory can deal with the uncertainty of decision process. Finally, the experimental results illustrate the superiority of FN3WD and verify the effectiveness of the proposed framework of the extended LMADM by using some benchmarked datasets and the Commercial Modular Aero-Propulsion System Simulation dataset.

属性的权重分配被视为多属性决策（MADM）的关键部分，这也适用于标记多属性决策（LMADM），这是专门针对带有标签的数据集提出的决策理论。但是，关于以冗余和不确定性为特征的海量数据的决策，应考虑使用更多方法，包括属性选择和不确定性处理来解决这些问题。本文基于LMADM的传统框架，推导了一种适应海量数据决策的新框架。针对由数据和决策过程产生的不确定性，提出了一种模糊邻域三向决策模型（FN3WD），其中模糊邻域关系可以解决数据的不确定性，三向决策理论可以处理决策过程的不确定性。最后，实验结果说明了FN3WD的优越性，并通过使用一些基准数据集和商业模块化航空推进系统仿真数据集，验证了所提出的扩展LMADM框架的有效性。