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As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]