Zobrazeno 1 - 10
of 108 112
pro vyhledávání: '"Xgboost"'
Publikováno v:
Resources Development & Market. 2024, Vol. 40 Issue 9, p1398-1409. 12p.
Publikováno v:
Shanghai Land & Resources / Shanghai Guotu Ziyuan. Jun2024, Vol. 45 Issue 2, p44-47. 4p.
Publikováno v:
Israeli Journal of Aquaculture-Bamidgeh. 2024, Vol. 76 Issue 4, p1-12. 12p.
Autor:
Zhao, Weiguang1,2 (AUTHOR), Sang, Shuxun1,3,4 (AUTHOR) shxsang@cumt.edu.cn, Han, Sijie3,4 (AUTHOR) shxsang@cumt.edu.cn, Cheng, Deqiang5 (AUTHOR), Zhou, Xiaozhi1,6 (AUTHOR), Guo, Zhijun7,8 (AUTHOR), Zhao, Fuping7,8 (AUTHOR), Zhang, Jinchao1 (AUTHOR), Gao, Wei9 (AUTHOR)
Publikováno v:
Energies (19961073). Dec2024, Vol. 17 Issue 23, p6060. 16p.
Autor:
Hidayaturrohman, Qisthi Alhazmi1,2 (AUTHOR) 23805191@edu.cc.saga-u.ac.jp, Hanada, Eisuke3 (AUTHOR) hanada@cc.saga-u.ac.jp
Publikováno v:
BioMedInformatics. Dec2024, Vol. 4 Issue 4, p2201-2212. 12p.
As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the
Externí odkaz:
http://arxiv.org/abs/2412.16333
Impact of Sampling Techniques and Data Leakage on XGBoost Performance in Credit Card Fraud Detection
Autor:
Kabane, Siyaxolisa
Credit card fraud detection remains a critical challenge in financial security, with machine learning models like XGBoost(eXtreme gradient boosting) emerging as powerful tools for identifying fraudulent transactions. However, the inherent class imbal
Externí odkaz:
http://arxiv.org/abs/2412.07437
Autor:
Tur, Yalcin, Cicek, Vedat, Cinar, Tufan, Keles, Elif, Allen, Bradlay D., Savas, Hatice, Durak, Gorkem, Medetalibeyoglu, Alpay, Bagci, Ulas
Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies. Conventional clinical methods have limited success in predicting 3
Externí odkaz:
http://arxiv.org/abs/2411.18063
Phishing attacks remain a persistent threat to online security, demanding robust detection methods. This study investigates the use of machine learning to identify phishing URLs, emphasizing the crucial role of feature selection and model interpretab
Externí odkaz:
http://arxiv.org/abs/2411.06860
This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality o
Externí odkaz:
http://arxiv.org/abs/2411.05937