Статья
DOI:
Полный текст:
Данная статья посвящена описанию алгоритма решающих деревьев, применительно к задаче регрессии, учитывающих динамическую стабильность целевой переменной и данных в листьях. В статье приведен обзор существующих решений, в рамках которого обсуждаются недостатки известных подходов. Описаны возможные варианты функций потерь, по-разному учитывающих нестационарный характер независимых переменных относительно целевой переменной.
Ahmed A. M., Rızaner A., Ulusoy A. H. A Decision Tree Algorithm Combined with Linear Regression for Data Classification. 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). (Sudan, Khartoum, 12 - 14 August 2018).
Baier L., Hofmann M., Kühl N., Mohr M., Satzger G. Handling Concept Drifts in Regression Problems - the Error Intersection Approach. Proceed of 15[th] International Conference on Wirtschaftsinformatik (WI2020 Zentale Tracks) (Germany, Potsdam), pp. 210 - 224. Gacar B. K., Kocakoç İ. D. Regression Analyses or Decision Trees? anisa e lal a ar iv ersitesi os al i limler e rgisi, 2020, vol. 18 (4), pp. 251 - 260.
Grabczewski K., Jankowski N. Feature selection with decision tree criterion. Fifth International Conference on Hybrid Intelligent Systems (HIS'05). (Brazil, Rio de Janeiro, 6 - 9 November 2005). Rio de Janeiro, 2005.
Ikonomovska E., Gama J., Sebastião R., Gjorgjevik D. Regression Trees from Data Streams with Drift Detection. Lecture notes in computer science (LNAI), 2009, vol. 5808, pp. 121 - 135.
Kassim N. M., Santhiran S., Alkahtani A. A., Islam M. A., Tiong S. K., Yusof M. M., Amin N. An adaptive decision tree regression modeling for the output power of Large-Scale Solar (LSS) farm forecasting. Sustainability, 2023, vol. 15 (18).
Kushwah J. S., Kumar A., Patel S. C., Soni R., Gawande A., Gupta S. Comparative study of regressor and classifier with decision tree using modern tools. Materials Today Proceedings, 2023, vol. 56 (6), pp. 3571 - 3576.
Lima M., Neto M. A., Filho T. S., de a Fagundes R. Learning Under Concept Drift for Regression - A Systematic Literature Review. IEEE Access, 2022, vol. 10, pp. 45410 - 45429.
Loh W. Classification and regression trees. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 2011, vol. 1 (1), pp. 14 - 23.
Loh W. Fifty years of classification and regression trees. International Statistical Review, 2014, vol. 82 (3), pp. 329 - 348.
Pietruczuk, L., Duda, P., Jaworski M. Adaptation of decision trees for handling concept drift. Lecture notes in computer science (LNAI), 2013, vol. 7894, pp. 459 - 473.
Rzazade U., Deryabin S., Temkin I., Kondratev E., Ivannikov A. On the Issue of the Creation and Functioning of Energy Efficiency Management Systems for Technological Processes of Mining Enterprises. Energies, 2023, vol. 16 (13), pp. 1 - 21.
Temkin I., Klebanov D., Deryabin S., Konov I. Construction of intelligent geoinformation system for a mine using forecasting analytics techniques. Gornyj Informacionno-analitičeskij û lletenʹ, 2020, vol. 3, pp. 114 - 125.
Yurdakul B., Naranjo J. D. Statistical properties of the population stability index. The Journal of Risk Model Validation, 2020, vol. 14, no. 4, pp. 89 - 100.
Zhu L., Liu G., Chen D., Chen Z., Li X. An Intelligent Boosting and Decision-Tree-RegressionBased Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching. Information, 2023, vol. 14 (6), pp. 1 - 13.
Baier L., Hofmann M., Kühl N., Mohr M., Satzger G. Handling Concept Drifts in Regression Problems - the Error Intersection Approach. Proceed of 15[th] International Conference on Wirtschaftsinformatik (WI2020 Zentale Tracks) (Germany, Potsdam), pp. 210 - 224. Gacar B. K., Kocakoç İ. D. Regression Analyses or Decision Trees? anisa e lal a ar iv ersitesi os al i limler e rgisi, 2020, vol. 18 (4), pp. 251 - 260.
Grabczewski K., Jankowski N. Feature selection with decision tree criterion. Fifth International Conference on Hybrid Intelligent Systems (HIS'05). (Brazil, Rio de Janeiro, 6 - 9 November 2005). Rio de Janeiro, 2005.
Ikonomovska E., Gama J., Sebastião R., Gjorgjevik D. Regression Trees from Data Streams with Drift Detection. Lecture notes in computer science (LNAI), 2009, vol. 5808, pp. 121 - 135.
Kassim N. M., Santhiran S., Alkahtani A. A., Islam M. A., Tiong S. K., Yusof M. M., Amin N. An adaptive decision tree regression modeling for the output power of Large-Scale Solar (LSS) farm forecasting. Sustainability, 2023, vol. 15 (18).
Kushwah J. S., Kumar A., Patel S. C., Soni R., Gawande A., Gupta S. Comparative study of regressor and classifier with decision tree using modern tools. Materials Today Proceedings, 2023, vol. 56 (6), pp. 3571 - 3576.
Lima M., Neto M. A., Filho T. S., de a Fagundes R. Learning Under Concept Drift for Regression - A Systematic Literature Review. IEEE Access, 2022, vol. 10, pp. 45410 - 45429.
Loh W. Classification and regression trees. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 2011, vol. 1 (1), pp. 14 - 23.
Loh W. Fifty years of classification and regression trees. International Statistical Review, 2014, vol. 82 (3), pp. 329 - 348.
Pietruczuk, L., Duda, P., Jaworski M. Adaptation of decision trees for handling concept drift. Lecture notes in computer science (LNAI), 2013, vol. 7894, pp. 459 - 473.
Rzazade U., Deryabin S., Temkin I., Kondratev E., Ivannikov A. On the Issue of the Creation and Functioning of Energy Efficiency Management Systems for Technological Processes of Mining Enterprises. Energies, 2023, vol. 16 (13), pp. 1 - 21.
Temkin I., Klebanov D., Deryabin S., Konov I. Construction of intelligent geoinformation system for a mine using forecasting analytics techniques. Gornyj Informacionno-analitičeskij û lletenʹ, 2020, vol. 3, pp. 114 - 125.
Yurdakul B., Naranjo J. D. Statistical properties of the population stability index. The Journal of Risk Model Validation, 2020, vol. 14, no. 4, pp. 89 - 100.
Zhu L., Liu G., Chen D., Chen Z., Li X. An Intelligent Boosting and Decision-Tree-RegressionBased Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching. Information, 2023, vol. 14 (6), pp. 1 - 13.
Ключевые слова:
регрессия, решающие деревья, динамическая стабильность, машинное обучение, регуляризация
Для цитирования:
Мышлянов А. В., Темкин И. О. Алгоритм построения стабильных деревьев в задаче регрессии // Вестник Череповецкого государственного университета. 2024. № 5 (122). С. 67–73. https://doi.org/10.23859/1994-0637-2024-5-122-6
Контент доступен под лицензией Creative Commons Attribution 4.0 License.