THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting by Liqian Zhou, Jiao Song, Zejun Li, Yingxi Hu, Wenyan Guo

“…Finally, it devises the histogram-based gradient boosting model to capture high-quality LRIs. …”
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Using gradient boosting trees to predict the costs of forwarding contracts by Sławomir Pioroński, Tomasz Górecki

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Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts by Dymitr Ruta, Ming Liu, Ling Cen, Quang Hieu Vu

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Extreme Gradient Boosting Regressor Solution for Defy in Drilling of Materials by Sangeetha Elango, Elango Natarajan, Kaviarasan Varadaraju, Ezra Morris Abraham Gnanamuthu, R. Durairaj, Karthikeyan Mohanraj, M. A. Osman

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Mixed effect gradient boosting for high-dimensional longitudinal data by Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

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Centrifugal Force Boosts Self‐Assembly of Gradient Lattice Photonic Crystals by Jingying Zhang, Xinye Wang, Yexiaotong Zhang, Jiaxing Yang, Xuelei Han

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Fake Fingerprint Classification Using Hybrid Features Learning With Gradient Boosting by Muhammad Salman Ali, Arslan Akram, Javed Rashid, Muhammad Arfan Jaffar, Dilawar Shah, Shujaat Ali, Muhammad Tahir

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Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials by Mahmood Ahmad, Ramez A. Al-Mansob, Kazem Reza Kashyzadeh, Suraparb Keawsawasvong, Mohanad Muayad Sabri Sabri, Irfan Jamil, Arnold C. Alguno

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Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees by Manley, William, Tran, Tam, Prusinski, Melissa, Brisson, Dustin

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A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1) by N. Maffezzoli, N. Maffezzoli, N. Maffezzoli, E. Rignot, E. Rignot, C. Barbante, C. Barbante, T. Petersen, S. Vascon

“…IceBoost is an ensemble of two gradient-boosted trees trained with 3.7 million globally available ice thickness measurements and an array of 39 numerical features. …”
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Ensembles of Gradient Boosting Recurrent Neural Network for Time Series Data Prediction by Shiqing Sang, Fangfang Qu, Pengcheng Nie

“…In this paper, Ensembles of Gradient Boosting Recurrent Neural Network (EGB-RNN) is proposed, which combines the gradient boosting ensemble framework with three types of recurrent neural network models, namely Minimal Gated Unit (MGU), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). …”
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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions by R. L. Bailey, M. A. Reiss, C. N. Arge, C. Möstl, C. J. Henney, M. J. Owens, U. V. Amerstorfer, T. Amerstorfer, A. J. Weiss, J. Hinterreiter

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Studying the impact of the Green Deal on the EU economy using Gradient Boosting Algorithm by Frahi Fadila

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Investigating the contributors to hit-and-run crashes using gradient boosting decision trees. by Baorui Han, Haibo Huang, Gen Li, Chenming Jiang, Zhen Yang, Zhenjun Zhu

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Extreme Gradient Boosting Beats In-Silico Identification of Proteins Potentially Associated With Alzheimer’s by Sadia Khalil, Wajid Arshad Abbasi, Syed Ali Abbas, Maryum Bibi, Saiqa Andleeb, Amsa Shabir

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Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree by Shuai Jiang, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam, Yasuhiko Morimoto

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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

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Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer by Xiuxiu Yang, Debolina Chatterjee, Justin L. Couetil, Ziyu Liu, Valerie D. Ardon, Chao Chen, Jie Zhang, Kun Huang, Kun Huang, Kun Huang, Travis S. Johnson, Travis S. Johnson, Travis S. Johnson

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