Results 11 to 20 of about 95,729 (269)

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
openaire   +2 more sources

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
openaire   +1 more source

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 ...
Chen Wang, Chengyuan Deng, Suzhen Wang
openaire   +2 more sources

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
openaire   +3 more sources

Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection

, 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.
Babayomi, Muyiwa, Olagbaju, Oluwatosin Atinuke, Kadiri, Abdulrasheed Adedolapo   +2 more
openaire   +2 more sources

SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION [PDF]

Proceedings on Engineering Sciences
Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading cause of female mortality. The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error prone.
Nageswara Rao Gali , Panduranga Vital Terlapu , Yasaswini Mandavakuriti, Sai Manoj Somu , Madhavi Varanasi , Vijay Telugu , Maheswara Rao V V R   +6 more
doaj   +1 more source

dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost

Methods, 2022
Promoters play an irreplaceable role in biological processes and genetics, which are responsible for stimulating the transcription and expression of specific genes. Promoter abnormalities have been found in some diseases, and the level of promoter-binding transcription factors can be used as a marker before a disease occurs.
Hongfei, Li, Lei, Shi, Wentao, Gao, Zixiao, Zhang, Lichao, Zhang, Yuming, Zhao, Guohua, Wang   +6 more
openaire   +2 more sources

Predicting time to graduation at a large enrollment American university

, 2020
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend ...
Aiken, John M., Caballero, Marcos D., De Bin, Riccardo, Hjorth-Jensen, Morten   +3 more
core   +1 more source

Sales Forecasting using XGBoost

, 2022
<p>This study intends to investigate several machine learning algorithms for sales forecasting strategies. A retailer can use this to predict future market demand and adjust its inventory levels accordingly. The accuracy of these predictions will determine whether the retailer profits or suffers losses.
Siddharth Anoop Srivastava, Dr K. Alice, Syed Hamad ul Haq Andrabi   +2 more
openaire   +1 more source

A Semi-Supervised Abbreviation Disambiguation Method Based on ACNN and Bi-LSTM

Journal of Harbin University of Science and Technology, 2022
In order to improve disambiguation accuracy of biomedical abbreviations， a semi-supervised abbreviation disambiguation method based on asymmetric convolutional neural networks and bidirectional long short term memory networks is proposed. Abbreviation is
ZHANG Chun-xiang, PANG Shu-yang, GAO Xue-yao   +2 more
doaj   +1 more source