Attribute and Scale Selection Based on Test Cost in Consistent Multi-scale Decision Systems
WU Di1,2,3,4, LIAO Shujiao1,2,3,4, FAN Yiwen1,2,3,4
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000; 3. Institute of Meteorological Big Data-Digital Fujian, Minnan Normal University, Zhangzhou 363000; 4. Fujian Key Laboratory of Data Science and Statistics, Minnan Normal University, Zhangzhou 363000
Abstract：The processing of multi-scale decision systems can simplify the complex problem, and simultaneous selection of attributes and scales is an important method in this process. In addition, the influence of cost factors is often taken into consideration in practical data processing. However, there is no research on cost factors in the simultaneous selection of attributes and scales. To solve this problem, the method of attribute and scale selection based on test cost in consistent multi-scale decision systems is proposed in this paper. Firstly, a corresponding rough set theoretical model is constructed. Both attribute and scale are considered in definitions and properties of the constructed theoretical model, and the test cost-based attribute-scale significance function is provided. Then, on the basis of concepts and properties of rough set applicable to multi-scale decision systems, a heuristic algorithm for simultaneous selection of attributes and scales is proposed. Experiments on UCI dataset show that the proposed algorithm significantly reduces the total test cost and improves computational efficiency.
吴迪, 廖淑娇, 范译文. 协调多尺度决策系统中基于测试代价的属性与尺度选择[J]. 模式识别与人工智能, 2023, 36(5): 433-447.
WU Di, LIAO Shujiao, FAN Yiwen. Attribute and Scale Selection Based on Test Cost in Consistent Multi-scale Decision Systems. Pattern Recognition and Artificial Intelligence, 2023, 36(5): 433-447.
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2023, Vol. 36 
