| Autor: |
Roman Kuzmich, Katerina Ponomareva, Artur Nikiforov |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
ITM Web of Conferences, Vol 59, p 03009 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2271-2097 |
| DOI: |
10.1051/itmconf/20245903009 |
| Popis: |
For efficient operation of machine learning methods, it is necessary to set their parameters up properly. Both the computational complexity and the accuracy of the method for the problem being solved can depend on the selected parameter values. The paper discusses the method of logical analysis of data, which is used to solve classification problems and consists of a number of steps. At each step, it is necessary to set the method parameters up to suit the problem being solved. When setting parameters, one should be guided by a compromise between the accuracy of the method and the computational complexity. With comparable values for classification accuracy of several variants of the method implementation, preference will always be given to the simplest of them in terms of computational complexity. Since the method works only with binary characteristics, at the first stage it is necessary to binarize quantitative characteristics. The binarization procedure is associated with the choice of the “number of binarization thresholds” parameter. This paper proposes an experimental approach to determine the best value of the specified method parameter for the problem being solved. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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