This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Zhang Yu, Zhang Yansong, Chen Hong, Susan Wang. OLAP foreign key join algorithm for MIC coprocessor [J]. Journal of Software, 2017, 28(03):490–501.ZhangYuZhangYansongChenHongSusanWangOLAP foreign key join algorithm for MIC coprocessor [J]20172803490501Search in Google Scholar
Yang Guijun, Xu Xue, Zhao Fuqiang. User score prediction model based on XGBoost algorithm and its application [J]. Data Analysis and Knowledge Discovery, 2019, 3(01):118–126.YangGuijunXuXueZhaoFuqiangUser score prediction model based on XGBoost algorithm and its application [J]2019301118126Search in Google Scholar
Ye Qianyi, Rao Hong, Ji Mingshu. Commercial sales forecast based on Xgboost [J]. Journal of Nanchang University (Science Edition), 2017, 41(03):275–281.YeQianyiRaoHongJiMingshuCommercial sales forecast based on Xgboost [J]20174103275281Search in Google Scholar
Chen Zhenyu, Liu Jinbo, Jerry Lee, Ji Xiaohui, Li Dapeng, Huang Yunhao, Di Fangchun, Gao Xingyu, Xu Lizhong. Ultra-short term power load forecasting based on LSTM and XGBoost combined model [J]. Power Grid Technology, 2020, 44(02):614–620.ChenZhenyuLiuJinboJerryLeeJiXiaohuiLiDapengHuangYunhaoDiFangchunGaoXingyuXuLizhongUltra-short term power load forecasting based on LSTM and XGBoost combined model [J]20204402614620Search in Google Scholar
Zhang Chengchang, Zhang Huayu, Luo Jianchang, He Feng. Analysis method of massive electricity consumption data based on cloud computing and improved K-means algorithm [J]. Computer Applications, 2018, 38(01):159–164.ZhangChengchangZhangHuayuLuoJianchangHeFengAnalysis method of massive electricity consumption data based on cloud computing and improved K-means algorithm [J]20183801159164Search in Google Scholar
Liu Nian, Liu Yu. Research on visualization technology of massive relational data based on clustering analysis algorithm [J]. Electronic Design Engineering, 2018, 26(10):92–95.LiuNianLiuYuResearch on visualization technology of massive relational data based on clustering analysis algorithm [J]201826109295Search in Google Scholar
Cheng Xueqi, Jin Xiaolong, Wang Yuanzhuo, Guo Jiafeng, Zhang Tieying, Li Guojie. Overview of Big Data System and Analysis Technology [J]. Journal of Software, 2014, 25(09):1889–1908.ChengXueqiJinXiaolongWangYuanzhuoGuoJiafengZhangTieyingLiGuojieOverview of Big Data System and Analysis Technology [J]2014250918891908Search in Google Scholar
Zhou Yanjun, Wang Shuangcheng, Wang Hui. Research on classifier based on Bayesian network [J]. Journal of Northeast Normal University (Natural Science Edition), 2003(02):21–27.ZhouYanjunWangShuangchengWangHuiResearch on classifier based on Bayesian network [J]2003022127Search in Google Scholar
Xuanxuan Lin. Research on Enterprise Bankruptcy Prediction Method Based on XGBOOST Model [A]. Wuhan Zhicheng Times Cultural Development Co., Ltd. proceedings of 4th international conference on e-education, e-business and information management (EEIM 2021) [c]. Wuhan Zhicheng Times Cultural Development Co., Ltd.: Wuhan Zhicheng Times Cultural Development Co., Ltd., 2021:8.LinXuanxuanWuhan Zhicheng Times Cultural Development Co., Ltd.: Wuhan Zhicheng Times Cultural Development Co., Ltd20218Search in Google Scholar
Shenglong Li, Xiaojing Zhang. Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm [J]. Neural Computing and Applications, 2020, 32(prepublish):LiShenglongZhangXiaojingResearch on orthopedic auxiliary classification and prediction model based on XGBoost algorithm [J]202032(prepublish):Search in Google Scholar
Feng Chen, Chen Zhide. Application of xgboost and LSTM weighted combination model in sales forecast [J]. Computer system application, 2019, 28 (10): 226–232. Doi: 10.15888/j.cnki.csa.007091ChenFengZhideChenApplication of xgboost and LSTM weighted combination model in sales forecast [J]2019281022623210.15888/j.cnki.csa.007091Open DOISearch in Google Scholar
Shenglong Li, Xiaojing Zhang. Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm [J]. Neural Computing and Applications, 2020, 32(prepublish):LiShenglongZhangXiaojingResearch on orthopedic auxiliary classification and prediction model based on XGBoost algorithm [J]202032(prepublish):Search in Google Scholar
Wei Dong, Yimiao Huang, Barry Lehane, Guowei Ma. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring [J]. Automation in Construction, 2020, 114(C):DongWeiHuangYimiaoLehaneBarryMaGuoweiXGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring [J]2020114CSearch in Google Scholar
Chixiang Wang, Junqi Guo. A data-driven framework for learners’ cognitive load detection using ECG-PPG physiological feature fusion and XGBoost classification [J]. Procedia Computer Science, 2019, 147:WangChixiangGuoJunqiA data-driven framework for learners’ cognitive load detection using ECG-PPG physiological feature fusion and XGBoost classification [J]2019147Search in Google Scholar
Gao Yifan, Yu Wenzhe, Chao Pingfu, et al. Score prediction and recommendation based on comment analysis [J]. Journal of East China Normal University: Natural Science Edition, 2015 (3): 80–90. (Gao Yifan, Yu Wenzhe, Chao Pingfu, et al. Analyzing reviews for rating prediction and item recommendation [J]. Journal of East China Normal University: Natural Science, 2015 (3): 80–90.)GaoYifanYuWenzheChaoPingfuScore prediction and recommendation based on comment analysis [J]201538090(Gao Yifan, Yu Wenzhe, Chao Pingfu, et al. Analyzing reviews for rating prediction and item recommendation [J]. Journal of East China Normal University: Natural Science, 2015 (3): 80–90.)Search in Google Scholar
Li V., Costantino H., Rowland J., Yue L., Gupta S.. ML3 LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) Models for Predicting Depression-Related Work Impairment in US Working Adults [J]. Value in Health, 2021, 24(S1).LiV.CostantinoH.RowlandJ.YueL.GuptaS.ML3 LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) Models for Predicting Depression-Related Work Impairment in US Working Adults [J]202124S1Search in Google Scholar
Li V., Costantino H., Rowland J., Yue L., Gupta S.. ML3 LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) Models for Predicting Depression-Related Work Impairment in US Working Adults[J]. Value in Health, 2021, 24(S1).LiV.CostantinoH.RowlandJ.YueL.GuptaS.ML3 LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) Models for Predicting Depression-Related Work Impairment in US Working Adults[J]202124S1Search in Google Scholar
Deng Xiaoyi, Jin Chun, Han Qingping, et al. Collaborative filtering recommendation model based on situational clustering and user rating [J]. System engineering theory and practice, 2013, 33 (11):2945–2953. (Deng Xiaoyi, Jin Chun, Han Jim C, et al. Improved Collaborative Filtering Model Based on Context Clustering and User Ranking [J]. Systems Engineering —Theory & Practice, 2013, 33(11): 2945–2953.)DengXiaoyiJinChunHanQingpingCollaborative filtering recommendation model based on situational clustering and user rating [J]2013331129452953(Deng Xiaoyi, Jin Chun, Han Jim C, et al. Improved Collaborative Filtering Model Based on Context Clustering and User Ranking [J]. Systems Engineering —Theory & Practice, 2013, 33(11): 2945–2953.)Search in Google Scholar
Zhang Hongli, Liu Jiying, Yang Sinan, et al. Research on scoring prediction model based on Internet user comments [J]. Data analysis and knowledge discovery, 2017, 1 (8): 48–58ZhangHongliLiuJiyingYangSinanResearch on scoring prediction model based on Internet user comments [J]2017184858Search in Google Scholar
McLachlan P, Munzner T, Koutsofios E, et al. LiveRAC: interactive visual exploration of system management time-series data[C] //Proceeding of the 26th International Conference on Human Factors in Computing Systems. New York: ACM Press, 2008: 1483–1492.McLachlanPMunznerTKoutsofiosEProceeding of the 26th International Conference on Human Factors in Computing SystemsNew York: ACM Press200814831492Search in Google Scholar
Agryzkov T, Oliver J L, Tortosa L, et al. Analyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia, Spain [J]. International Journal of Geographical Information Science, 2014, 28(3/4): 479–495.AgryzkovTOliverJ LTortosaLAnalyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia, Spain [J]2014283/4479495Search in Google Scholar
Qiu X, Suganthan P N, Amaratunga G A J. Ensemble incremental learning random vector functional link network for short-term electric load forecasting [J]. Knowledge-Based Systems, 2018, 145(4):182–196.QiuXSuganthanP NAmaratungaG A JEnsemble incremental learning random vector functional link network for short-term electric load forecasting [J]20181454182196Search in Google Scholar
Wang J, Lou C, Yu R, et al. Research on hot micro-blog forecast based on XGBOOST and random forest [M]. Knowledge Science, Engineering and Management, KSEM 2018, Lecture Notes in Computer Science, Springer.WangJLouCYuRKnowledge Science, Engineering and Management, KSEM 2018, Lecture Notes in Computer ScienceSpringerSearch in Google Scholar
Li C, Chen Z. Y, Liu J. B, et al. Power load forecasting based on the combined model of LSTM and XGBoost [C]//PRAI’ 19:Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence. Wenzhou, China: ACM, 2019: 46–51.LiCChenZ. YLiuJ. BPRAI’ 19:Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial IntelligenceWenzhou, China: ACM20194651Search in Google Scholar
Gómez-Ríos A, Luengo J, Herrera F. A study on the noise label influence in boosting algorithms: Adaboost, GBM and XGBoost [C]//International Conference on Hybrid Artificial Intelligence Systems (HAIS), 2017:268–280.Gómez-RíosALuengoJHerreraFInternational Conference on Hybrid Artificial Intelligence Systems (HAIS)2017268280Search in Google Scholar
Yue Yanchun, Huang Tingzhu. Error reciprocal variable weight combination prediction method [J]. Journal of University of Electronic Science and technology, 2007 (S1): 349–351.YueYanchunHuangTingzhuError reciprocal variable weight combination prediction method [J]2007S1349351Search in Google Scholar
Cao y B, Xu J, Liu T Y, et al. Adapting ranking SVM to document retrieval[C] //Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2006: 186–193.Cao yBXuJLiuT YProceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information RetrievalNew York: ACM Press2006186193Search in Google Scholar
Xu P P, Mei H H, Ren L, et al. ViDX: visual diagnostics of assembly line performance in smart factories [J]. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(1):291–300.XuP PMeiH HRenLViDX: visual diagnostics of assembly line performance in smart factories [J]2017231291300Search in Google Scholar
Sun Guanglu, song Zhichao, Liu Jinlai, Zhu Suxia, he Yongjun. Feature selection method based on maximum information coefficient and approximate Markov blanket [J]. Journal of automation, 2017, 43 (05): 795–805. Doi: 10.16383/j.aas.2017.c150851.SunGuanglusongZhichaoLiuJinlaiZhuSuxiaheYongjunFeature selection method based on maximum information coefficient and approximate Markov blanket [J]2017430579580510.16383/j.aas.2017.c150851Open DOISearch in Google Scholar
Zhang Li, Yuan Yuyu, Wang Zong. Fcbf feature selection algorithm based on maximum correlation information coefficient [J]. Journal of Beijing University of Posts and telecommunications, 2018, 41 (04): 86–90. Doi: 10.13190/j.jbupt.2017-229.ZhangLiYuanYuyuWangZongFcbf feature selection algorithm based on maximum correlation information coefficient [J]20184104869010.13190/j.jbupt.2017-229Open DOISearch in Google Scholar