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Extreme Gradient Boosting for Cyberpropaganda Detection
2021Propaganda, defamation, abuse, insults, disinformation and fake news are not new phenomena and have been around for several decades. However, with the advent of the Internet and social networks, their magnitude has increased and the damage caused to individuals and corporate entities is becoming increasingly greater, even irreparable. In this paper, we
Jaouhar Fattahi +2 more
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Extreme Gradient Boosting for Yield Estimation Compared With Deep Learning Approaches
Computers and Electronics in Agriculture, 2022 Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development.
Volker H. Steinhage, Florian Huber
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Self-trained eXtreme Gradient Boosting Trees
2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019Semi-Supervised Learning (SSL) is an ever-growing research area offering a powerful set of methods, either single or multi-view, for exploiting both labeled and unlabeled instances in the most effective manner. Self-training is a representative SSL algorithm which has been efficiently implemented for solving several classification problems in a wide ...
Nikos Fazakis +4 more
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Extreme Gradient Boosting and Behavioral Biometrics
Proceedings of the AAAI Conference on Artificial Intelligence, 2017As insider hacks become more prevalent it is becoming more useful to identify valid users from the inside of a system rather than from the usual external entry points where exploits are used to gain entry. One of the main goals of this study was to ascertain how well Gradient Boosting could be used for prediction or, in this case ...
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Stock Selection Based on Extreme Gradient Boosting
2019 Chinese Control Conference (CCC), 2019In 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
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Predicting lncRNA-disease Association based on Extreme Gradient Boosting
Proceedings of the 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics, 2020There 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 +3 more
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Electricity Theft Detection Base on Extreme Gradient Boosting in AMI
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020Metering 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
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Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
Journal of Chemical Information and Modeling, 2016In 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 +4 more
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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), 2020Web 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
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