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Pattern Recognition and Artificial Intelligence  2024, Vol. 37 Issue (4): 368-382    DOI: 10.16451/j.cnki.issn1003-6059.202404007
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Test Cost Sensitive Simultaneous Selection of Attributes and Scales in Multi-scale Decision Systems
LIAO Shujiao1,2,3,4, WU Di1,2,3,4, LU Yaqian1,2,3,4, FAN Yiwen1,2,3,4
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000;
2. Fujian Key Laboratory of Granular Computing and Applications, 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

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Abstract  

Multi-scale decision system is one of hot issues in the field of data mining, and cost factors appear frequently in data mining. A method for simultaneous selection of attributes and scales can effectively solve the knowledge reduction problem of multi-scale decision systems involving cost factors. However, in the existing research, there are few studies on the simultaneous selection of attributes and scales based on costs, and most of the algorithms only focus on consistent or inconsistent multi-scale decision systems. To address this issue, a test cost sensitive method for simultaneously selecting attributes and scales is proposed with the goal of minimizing the total test cost of data processing. The method is applicable to both consistent and inconsistent multi-scale decision systems. Firstly, a theoretical model is constructed based on rough set. In the model, both the attribute factor and the scale factor are taken intoaccount by concepts and properties. Secondly, a heuristic algorithm is designed based on the theoretical model. By the proposed algorithm, attribute reduction and scale selection can be simultaneously performed in the multi-scale decision systems based on test costs, and different attributes can choose different scales. Finally, the experiments verify the effectiveness, practicality and superiority of the proposed algorithm.
Key words： Attribute and Scale Selection      Cost-Sensitive Learning      Multi-scale Decision System      Rough Set      Monotonicity     
Received: 24 January 2024     
ZTFLH: TP 18  
Fund:

National Natural Science Foundation of China(No.12101289)
Corresponding Authors: LIAO Shujiao, Ph.D., professor. Her research interests include granular computing, data mining and artificial intelligence.   
About author:: WU Di, Master student. Her research interests include rough set, granular computing and data mining. LU Yaqian, Master student. Her research interests include rough set, granular computing and data mining. FAN Yiwen, Master student. Her research interests include rough set, granular computing and data mining.
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LIAO Shujiao
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Cite this article:   
LIAO Shujiao,WU Di,LU Yaqian等. Test Cost Sensitive Simultaneous Selection of Attributes and Scales in Multi-scale Decision Systems[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(4): 368-382.
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