Results 221 to 230 of about 126,268 (270)

Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 2020
Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers.
J Dutta, Yong Woon Kim, Dalia Dominic
openaire   +1 more source

Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions.

Bioresource Technology, 2023
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics.
Sheng Su, Juan Wang
semanticscholar   +1 more source

Stock Selection Based on Extreme Gradient Boosting

2019 Chinese Control Conference (CCC), 2019
In this paper, we established a multi-factor stock selection model based on Extreme Gradient Boosting (XGBoost) to beat the benchmark. We used accounting indicators, valuation indicators, emotions and technical indicators, 62 in total, as the feature space of the XGBoost classifier, and attempted to identify stocks from CSI 300 Index that are likely to
Xiaoyun Zhang, Wanyi Chen
openaire   +1 more source

State of Charge Estimation for Lithium-Ion Batteries at Various Temperatures by Extreme Gradient Boosting and Adaptive Cubature Kalman Filter

IEEE Transactions on Instrumentation and Measurement
State of charge (SOC) plays a crucial role in battery management systems (BMSs) as it significantly impacts battery lifespan and energy efficiency.
Weilu Hou, Qin Shi, Yiwen Liu, Liquan Guo, Xiaonan Zhang, Ji Wu   +5 more
semanticscholar   +1 more source

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting

Journal of Biomolecular Structure and Dynamics, 2023
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits.
Omar Alghushairy, Farman Ali, Wajdi Alghamdi, Majdi Khalid, Raed Alsini, Othman Asiry   +5 more
semanticscholar   +1 more source

Electricity Theft Detection Base on Extreme Gradient Boosting in AMI

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020
Metering data from the advanced metering infrastructure can be used to find abnormal electricity behavior for the detection of electricity theft, which causes huge financial losses to electric companies every year. This article proposes an electricity theft detector using metering data based on extreme gradient boosting (XGBoost). The metering data are
Zhongzong Yan, He Wen 0003
openaire   +1 more source

Predicting lncRNA-disease Association based on Extreme Gradient Boosting

Proceedings of the 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics, 2020
There is increasing evidence that long non-coding RNAs (lncRNAs) play an important role in many significant biological processes. Associations' detection between lncRNAs and human diseases by computational models is beneficial to the identification of biomarkers and the discovery of drugs for the diagnosis, treatment, and prognosis of human diseases ...
Xi Tang, Menglu Li, Wei Zhang, Junfeng Xia   +3 more
openaire   +1 more source

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

Journal of Chemical Information and Modeling, 2016
In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally.
Robert P. Sheridan, Wei Min Wang, Andy Liaw, Junshui Ma, Eric M. Gifford   +4 more
openaire   +2 more sources

An Extreme Gradient Boosting-based Prediction for Depression

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023
Ahmed Ibrahum, Kwang-Ho Park, Jang-Eui Hong, Van-Huy Pham 0001, Keun Ho Ryu   +4 more
openaire   +1 more source

Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)

The Arabian journal for science and engineering, 2022
T. Kavzoglu, A. Teke
semanticscholar   +1 more source