IoT-based generalized multi-granulation sequential three-way decisions

【Author】 Zhang, Yongjing; Li, Guannan; Dai, Wangchen; Hong, Chengxin; Qian, Jin; Han, Zhaoyang

【Source】COMPUTER COMMUNICATIONS

【影响因子】5.047

【Abstract】In the context of the Internet of Things (IoT), decision models and GRC (Governance, Risk, and Compliance) can be used for data processing and decision-making processes. The IoT involves a large number of sensors and devices, some of which enable "device-free"functionality, meaning that specific devices or sensors are not required to obtain the necessary information. This technology utilizes the signals present in the environment to determine the location of individuals without them carrying any specific devices. By incorporating "devicefree"sensor data into decision models, we can process IoT data more efficiently and achieve accurate location positioning. The prevailing hierarchical sequential three-way decision models (STDM) follow a common pattern of constructing a hierarchical tree of conditional attributes to acquire knowledge. However, these models fail to meet the diverse needs of users when it comes to rule mining complex hierarchical data with multiple levels and views in real-world applications. Additionally, the pursuit of simplified rules remains a significant topic of interest. To address these challenges, we propose a novel approach that integrates multi-granularity, device-free localization, and STDM. By leveraging IoT and device-free localization, we collect data from various devices and sensors to accurately determine the positions of individuals. Using this data, we construct a generalized hierarchical decision table by creating a concept hierarchical tree with decision attributes. This framework offers a comprehensive solution for overcoming the challenges of device-free localization and IoT. Subsequently, we establish a granular structure by partitioning the complete set of attributes. In this paper, our proposed approach improves the efficiency and accuracy of data processing in machine learning models and enables users to better process and understand complex datasets. Finally, the performance is experimentally validated for the models in this paper. Such models greatly enrich the theoretical framework of three-way decision making. Overall, the abstract provides a concise and succinct overview, outlining the motivation behind the research and presenting the contributions made.

【Keywords】Hierarchical rough set model; Multi-granulation; Sequential three-way decisions

【发表时间】2023 1-Sep

【收录时间】2023-09-02

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