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A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning.

C. Qin, S. Liu, S. Lin, G. Li, и J. Hong. DMBD (2), том 1454 из Communications in Computer and Information Science, стр. 217-227. Springer, (2021)

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Jiaming Song

Moon-Shin Hong

Jung-soo Hong

Xin Hong

Kang-hun Hong

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Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro -Fuzzy Inference System.Y. Fei, X. Huang, Q. Chen, J. Chen, L. Li, J. Hong, Z. Hao, и H. Wei. BIBM, стр. 1938-1942. IEEE, (2020)Research on the Design of Active Learning Algorithm based on Query-by-Committee for Intelligent Fetal Monitoring.B. Quan, M. Yang, X. Li, Q. Chen, G. Liu, J. Hong, Z. Hao, L. Li, и H. Wei. BIBM, стр. 515-521. IEEE, (2021)Joint optic disc and cup segmentation using semi-supervised conditional GANs.S. Liu, J. Hong, X. Lu, X. Jia, Z. Lin, Y. Zhou, Y. Liu, и H. Zhang. Comput. Biol. Medicine, (2019)Intelligent classification of antepartum cardiotocography model based on deep forest.Y. Chen, A. Guo, Q. Chen, B. Quan, G. Liu, L. Li, J. Hong, H. Wei, и Z. Hao. Biomed. Signal Process. Control., (2021)Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.L. Huang, Z. Jiang, R. Cai, L. Li, Q. Chen, J. Hong, Z. Hao, и H. Wei. BMC Medical Informatics Decis. Mak., 21 (1): 355 (2021)Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest.J. Chen, X. Liu, H. Wei, Q. Chen, J. Hong, Q. Li, и Z. Hao. ICSH, том 11924 из Lecture Notes in Computer Science, стр. 75-85. Springer, (2019)Association rule analysis for fetal heart rate pattern of late FGR.L. Zhong, S. Huang, X. Li, G. Liu, Q. Chen, X. Luo, Y. Hao, J. Hong, и H. Wei. BIBM, стр. 2964-2968. IEEE, (2022)A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning.C. Qin, S. Liu, S. Lin, G. Li, и J. Hong. DMBD (2), том 1454 из Communications in Computer and Information Science, стр. 217-227. Springer, (2021)Towards Making More Reliable Cardiotocogram Data Prediction with Limited Expert Knowledge: Exploiting Unlabeled Data with Semi-supervised Boosting Method.J. Hong, C. Qin, Y. Huang, и Y. Zhou. DMBD (1), том 1453 из Communications in Computer and Information Science, стр. 422-435. Springer, (2021)Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion.J. Feng, J. Liang, Z. Qiang, X. Li, Q. Chen, G. Liu, J. Hong, Z. Hao, и H. Wei. BIBM, стр. 2667-2673. IEEE, (2021)

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