NOMAQBUL ELEKTRON POCHTA XABARLARINI ANIQLASHNING ANSAMBL USULI
Keywords:
nomaqbul xabarlar, fishing, mashinaviy o‘qitish logistik regressiya, Sodda Bayes, SVM, k-NN, tasodifiy o‘rmon, ansambl.Abstract
E-mail systems remain one of the primary means of communication among users. However, unwanted messages pose a serious threat to system security and represent a significant risk to users and organizations. This paper discusses the principles of machine learning algorithms and presents a comparative analysis of their performance. Additionally, the advantages of ensemble methods for detecting and filtering unwanted messages are highlighted.
References
Mohammad, R. M. A. (2020). A lifelong spam emails classification model. Applied Computing and Informatics. Zamir, A., Khan, H. U., Mehmood, W., Iqbal, T., & Akram, A. U. (2020). A feature-centric spam email detection model using diverse supervised machine learning algorithms.
G.S. Karimovich, K.S. Jaloldin Ugli, O.I. Salimbayevich, “Analysis of machine learning methods for filtering spam messages in email services,” 2020 International Conference on Information Science and Communications Technologies, 2020.
S.K. Ganiev, S.J. Khamidov, “Artificial intelligence-based methods for filtering spam messages in email services,” 2021 nternational Conference on Information Science and Communications Technologies: Applications, Trends and Opportunities, 2021.
Klyueva I. A. Ensemble methods in the problem of multi-class SVM classification / B. V. Kostrov, A. I. Baranchikov, I. A. Klyueva // XXI century: results of the past and problems of the present plus. - 2021. - Vol. 10, No. 2 (54). - P. 105-108.
Kashnitsky Yu. S. Ensemble method of machine learning based on classifier recommendations / Yu. S. Kashnitsky, D. I. Ignatov // Intellectual systems. Theory and applications. - 2015. - Vol. 19, No. 4 - P. 1-32.
Additional Files
Published
How to Cite
License
Copyright (c) 2025 Шерзод Хамидов
This work is licensed under a Creative Commons Attribution 4.0 International License.
