A transfer-based decision-making method based on expert risk attitude and reliability

Abstract

Attributed to emerging information technologies in the current era, historical data have been gradually accumulated in the process of people making decisions, in which people’s preferences are characterized by the data. These accumulated data are beneficial for generating decision recommendations. A small volume of historical data, unfortunately, may not actually characterize people’s preferences and be difficult to generate convinced decision recommendations. To address decision-making problems in this context, a transfer-based decision-making method is proposed based on the idea of parameter transfer given that experts’ risk attitudes and reliabilities are adopted to characterize their preferences. Characterized by the orness degree in the ordered weighted averaging operator, an expert’s risk attitude is identified by minimizing the average distance between overall assessments and their predictions on the historical dataset. An expert’s decision accuracy and internal consistency are defined on the historical dataset and combined to identify the expert’s reliability. With the source domain selected by experts’ reliabilities, a transfer model is constructed, in which experts’ risk attitudes are transferred between source and target domains. The effectiveness of the proposed method is validated by its application in the auxiliary diagnosis of breast lesions, its comparison with different methods, and its ablation experiment.