Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs
Having the declarations used in customs procedures be submitted without errors is critical. In the face of the diversity, dynamism, and complexity of the methods used in creating this declaration, human-induced declaration files are produced erroneously. These cause many problems such as loss of lab...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Istanbul University Press
2022-12-01
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| Series: | Acta Infologica |
| Subjects: | |
| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/98C1855791714246A5D21D3D5CE71D87 |
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| Summary: | Having the declarations used in customs procedures be submitted without errors is critical. In the face of the diversity, dynamism, and complexity of the methods used in creating this declaration, human-induced declaration files are produced erroneously. These cause many problems such as loss of labor, customers, and money, as well as legal problems such as contract and legal compliance. Intelligent structures supported by current information technologies are needed to solve these problems. For this purpose, being able to use learning algorithms over big data is important in the field of customs declaration creation in the logistics industry. This study evaluates the efficiency performances of learning-based algorithms regarding the customs declaration process over 4,005,343 pieces of declaration data. According to the performance measurement results, the maximum result was achieved in the Decision Tree (75.69%) and Bagging (75.70%) algorithms with respect to the Train-test split method at a test rate of 25%. Regarding the K-Fold method, which assumes K to be equal to 10, similar success rates were obtained for the Decision Tree (75.84%) and Bagging (75.83%) algorithms. These results reveal the use of machine learning algorithms to be an effective method for detecting notification errors. This can be a resource for improving customs declaration processes and developing smart control structures, as well as for new studies to be carried out in the field. |
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| ISSN: | 2602-3563 |