Review on Multi-granulation Computing Models and Methods for Decision Analysis
PANG Jifang1, SONG Peng2, LIANG Jiye1,3
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006; 2. School of Economics and Management, Shanxi University, Taiyuan 030006; 3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：As the core concept and key technology of granular computing, multi-granulation computing emphasizes multi-view and multi-level understanding and description of real-world problems to obtain more reasonable and satisfactory results. The existing four types of multi-granulation computing models are firstly introduced, including multi-granulation rough set, multi-scale data analysis, sequential three-way decision and hierarchical classification learning, for the further effective fusion of multi-granulation computing and decision analysis and better satisfaction with actual decision-making needs. Then, their main characteristics and development process are expounded. Furthermore, the research status of decision analysis methods based on multi-granulation computing models is summarized from the aspects of attribute reduction, rule extraction, granularity selection, information fusion, group decision-making, multi-attribute group decision-making, classification decision-making and dynamic decision-making. Finally, some challenging research directions of intelligent decision-making in the era of big data are forecasted to promote the continuous development and innovation of multi-granulation intelligent decision-making.
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