Intuitionistic fuzzy three-way transfer learning based on rough almost stochastic dominance

As a significant extension of rough set theory, three-way decision (3WD) theory plays a crucial role in the data mining of uncertain information and decision-making analysis. Transfer learning (TL) is also a powerful knowledge discovery and deep learning strategy that has attracted the attention of many scholars. At present, many achievements have been made in related studies based on the concepts of sample transfer, feature transfer, and parameter transfer. However, there are few studies on multi-granularity fusion and TL for multisource data from the perspective of the stochastic dominance (SD) relation. In this paper, an intuitionistic fuzzy three-way transfer learning (IF3WTL) model based on rough almost stochastic dominance (RASD) is proposed. In this scenario, we first introduce the concept of a rough marginal information measure and the corresponding calculation method. Then, an RASD method is proposed to generate the multi-granularity distribution of same-category information in the source domain. In addition, 3WD theory is introduced to classify the target domain objects into positive, negative, and boundary regions based on the relation between marginal minimum risk information and the corresponding multi-granularity distribution. Furthermore, a secondary decision strategy with an SVM algorithm is implemented to iteratively process boundary region objects. The proposed method can reduce the differences in the data distribution among domains through RASD, improve noise tolerance, and obtain the degree of roughness of common information at different scales. Moreover, the proposed method adopts an iterative learning strategy, which can reduce the decision-making cost in cases with insufficient information and improve the accuracy of classification for objects in the boundary region. The rationality and effectiveness of the proposed model are verified through experiments with the ABIDE dataset and comparative analyses with the existing state-of-the-art methods.