Results 11 to 20 of about 53,639 (265)
Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost [PDF]
Pattern Recognition Letters, 2020 The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in terms of size, the package is, to the best of the authors' knowledge, the first of its kind which provides an integrated implementation ...
Suzhen Wang, Chen Wang
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Lithology Recognition Research Based on Wavelet Transform and Artificial Intelligence
Cejing jishu, 2023 Lithology identification is one of the main application directions of deep learning in oil and gas field development. Artificial intelligence models can effectively improve the efficiency of oil and gas field development and on-site construction.
FANG Dazhi +4 more
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Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection
CoRR, 2023 Brain tumors are masses or abnormal growths of cells within the brain or the central spinal canal with symptoms such as headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behaviour, nausea, vomiting, vision or hearing problems and dizziness.
Muyiwa Babayomi +2 more
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Statistics, Optimization & Information Computing, 2023 Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan,
HASSAN OUKHOUYA +3 more
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Dizhen xuebao, 2021 为了重构或恢复存在严重干扰或数据缺失的台站观测数据，本文基于周边已有台站的高质量观测数据采用XGBoost机器学习方法重构地磁日变数据。仿真试验结果显示，无论是磁静日还是磁扰日，地磁场分量的绝对残差均值均低于0.1 nT。试验统计数据及重构结果残差曲线的对比分析表明，地磁日变重构精度与地磁活动性和待重构信号的时变剧烈程度有关；相较于反向传播神经网络，XGBoost方法对地磁场日变数据的重构精度更高。本文研究表明，基于XGBoost机器学习的重构方法在处理非线性复杂问题方面具有优势 ...
Wenkai Cheng +3 more
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The Use of the XGBoost and Kriging Methods in the Prediction of the Microstructure of CGI Cast Iron [PDF]
Archives of Foundry Engineering, 2023 Compacted Graphite Iron (CGI), is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties ...
Łukasz Sztangret +4 more
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016 Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.
Tianqi Chen 0001, Carlos Guestrin
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A Study on Calories Burnt Prediction Using Machine Learning [PDF]
ITM Web of Conferences, 2023 In this growing technological era, People are less aware of their health and mental stability. Due to lack of time, they intake more junk food than healthy options, which leads to an increase in the total calorie rate in their body.
Panwar Punita +3 more
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Feature Interactions in XGBoost
CoRR, 2020 In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results show that accurate identification of these constraints can help improve the performance of baseline XGBoost model significantly.
Goyal, Kshitij +2 more
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Secure Collaborative Training and Inference for XGBoost [PDF]
Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020 In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations have also encountered obstacles in sharing sensitive data due to business, regulatory, and liability
Andrew Law +8 more
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