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XGBoost [PDF]

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.
Chen, Tianqi, Guestrin, Carlos
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Multiple Imputation Through XGBoost

Journal of Computational and Graphical Statistics, 2023
The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures.
Yongshi Deng, Thomas Lumley
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Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary\n Label-Imbalanced Classification with XGBoost [PDF]

Pattern Recognition Letters, 2019
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 ...
Chen Wang, Chengyuan Deng, Suzhen Wang
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Breast Cancer Classification using XGBoost

World Journal of Advanced Research and Reviews
Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause.
Rahmanul Hoque, Suman Das, Mahmudul Hoque, Ehteshamul Haque   +3 more
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Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions

IEEE Access
Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. However, recent studies on loss functions for imbalanced data have primarily focused on deep learning.
Akiyoshi Sutou, Jinfang Wang
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Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method [PDF]

Applied Sciences
The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources.
Jianting Zhang, Ruifei Wang, Ailin Jia, Naichao Feng   +3 more
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Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning [PDF]

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2021
Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine ...
Neha Sharma, Narendra Yadav, Saurabh Sharma   +2 more
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Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

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, HAMZA KADIRI, KHALID EL HIMDI, RABY GUERBAZ   +3 more
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Comparison of some non-linear functions to describe the growth for Linda geese with CART and XGBoost algorithms

Czech Journal of Animal Science, 2022
The aim of this study was to determine the best non-linear function describing the growth of the Linda goose breed. To achieve this aim, five non-linear functions, such as exponential, logistic, von Bertalanffy, Brody and Gompertz, were employed to ...
Cem Tirink, Hasan Önder, Sabri Yurtseven, Zeliha Kaya Akil   +3 more
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The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data

Mathematical Biosciences and Engineering, 2023
Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk ...
Xiaoyu Hou, Baoshan Ma, Ming Liu, Yuxuan Zhao, Bingjie Chai , Jianqiao Pan, Pengcheng Wang , Di Li , Shuxin Liu , Fengju Song   +9 more
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