Decision-theoretic rough sets (DTRSs), which can be considered as generalized rough set models produced by Bayesian risk minimum and three-way decisions (3WD) theories, have achieved fruitful results in risk decision-making problems. Nevertheless, the parameter determination of decision-theoretic rough sets is a challenging problem in practical applications, which narrows the generalization and development of these models. In this paper, a methodology to determine the parameters for DTRS and 3WD is proposed to improve their practicability. First, a data-driven loss function matrix is introduced based on the significance and the probability of the sample. Subsequently, a generalized rough set model named single-parameter decision-theoretic rough set (SPDTRS) is put forward based on the proposed loss function matrix. The main feature of the proposed model is that there is only one parameter that should be preset rather than the two or six parameters in the traditional DTRS models. Finally, some experiments on the University of California Irvine (UCI) and Knowledge Extraction based on Evolutionary Learning (KEEL) data sets are conducted to illustrate the effectiveness of the proposed methodology.

决策理论粗糙集（DTRS），可以看作是由贝叶斯最小风险和三向决策（3WD）理论产生的广义粗糙集模型，已经在风险决策问题上取得了丰硕的成果。然而，决策理论粗糙集的参数确定在实际应用中是一个具有挑战性的问题，这使这些模型的泛化和开发变得狭窄。本文提出了一种确定DTRS和3WD参数的方法，以提高其实用性。首先，基于样本的重要性和概率，引入了一个数据驱动的损失函数矩阵。随后，基于所提出的损失函数矩阵，提出了一种称为单参数决策理论粗糙集（SPDTRS）的广义粗糙集模型。所提出的模型的主要特征是，只有一个参数应该预设，而不是传统DTRS模型中的两个或六个参数。最后，对加州大学尔湾分校（UCI）和基于进化学习（KEEL）数据集的知识提取进行了一些实验，以说明所提出方法的有效性。