Tree retraining in the decision tree learning algorithm : доклад, тезисы доклада | Научно-инновационный портал СФУ

Tree retraining in the decision tree learning algorithm : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: III International Conference on Advanced Technologies in Aerospace, Mechanical and Automation Engineering - MIST: Aerospace-III-2020; 9-th International Workshop on Mathematical Models and their Applications (IWMMA-2020); Krasnoyarsk; Krasnoyarsk

Год издания: 2021

Идентификатор DOI: 10.1088/1757-899X/1047/1/012082

Аннотация: Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well. One of them is that decision tree learning algorithm build an “almost optimal” tree. The paper considers the way to improve the efficiency of decision trees. The paper proposes a modification of decision tree learning algorithms by retraining the part of tree at every node training. The classification problems were solved to compare standard decision tree learning algorithms with the modified ones. Algorithm efficiency refers to the percentage of correctly classified test sample objects. Statistical analysis based on Student's t-test was carried out to compare the efficiency of the algorithms.

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Издание

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: 1047

Номера страниц: 12082

Место издания: Krasnoyarsk, Russian Federation

Издатель: IOP Publishing Ltd

Авторы

  • Mitrofanov S A (Reshetnev Siberian State University of Science and Technology)
  • Semenkin E S (Reshetnev Siberian State University of Science and Technology)
  • Krasnoyarsk Science and Technology City Hall.

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