Abstract
In granular computing, a single conditional attribute is usually used as a view to describe the target concept, and each view can choose a specific level of granularity to describe the object in the hierarchical rough set model. However, the existing three-way decision model cannot combine multi-level and multi-view to make decisions, and these models are extremely complicated and difficult to apply. Within the multi-level data, how to obtain a certain decision from different levels and views is the most important issue. To this end, we propose a hierarchical multi-granulation sequential three-way decision model by combining multi-granularity and sequential three-way decisions. Specifically, we construct concept hierarchy tree of conditional attribute, then construct granular view under different levels of granularity, and update the information by multi-step three-way decision-making method. Finally, the experimental results demonstrate that the proposed model can mine the rules of hierarchical decision table. The model will improve the theoretical framework of hierarchical rough set model.
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Acknowledgments
The research is supported by the National Natural Science Foundation of China (Nos. 62066014, 62163016, 62166001), Double thousand plan of Jiangxi Province of China, Jiangxi Province Natural Science Foundation of China under Grant Nos. 20202BABL202018, 20212ACB202001, 20202BABy02010.
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Hong, C., Qian, J., Jiang, H., Tong, Z., Yu, Y., Liu, C. (2022). Hierarchical Multi-granulation Sequential Three-Way Decisions. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_25
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