Dynamic ensemble mechanisms to improve particulate matter forecasting
暂无分享,去创建一个
[1] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[2] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[3] Daiwen Kang,et al. Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States , 2011 .
[4] L. Vinet,et al. A ‘missing’ family of classical orthogonal polynomials , 2010, 1011.1669.
[5] Marcus A. Maloof,et al. Using additive expert ensembles to cope with concept drift , 2005, ICML.
[6] Majid Salari,et al. Statistical models for multi-step-ahead forecasting of fine particulate matter in urban areas , 2019, Atmospheric Pollution Research.
[7] G. N. Pillai,et al. Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS) , 2017, Appl. Soft Comput..
[8] Mark H Johnson,et al. The development of spatial frequency biases in face recognition. , 2010, Journal of experimental child psychology.
[9] G. M. Biju,et al. Chaotic time series prediction using ELANFIS , 2017, 2017 6th International Conference on Computer Applications In Electrical Engineering-Recent Advances (CERA).
[10] Abdullah Kadri,et al. Urban Air Pollution Monitoring System With Forecasting Models , 2016, IEEE Sensors Journal.
[11] Rui Araújo,et al. A dynamic and on-line ensemble regression for changing environments , 2015, Expert Syst. Appl..
[12] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[13] Rui Araújo,et al. An on-line weighted ensemble of regressor models to handle concept drifts , 2015, Eng. Appl. Artif. Intell..
[14] Saso Dzeroski,et al. Learning model trees from evolving data streams , 2010, Data Mining and Knowledge Discovery.
[15] Regression and multivariate models for predicting particulate matter concentration level , 2017, Environmental Science and Pollution Research.
[16] Ana Estela Antunes da Silva,et al. Using Ensembles of Artificial Neural Networks to Improve Pm10 Forecasts , 2015 .
[17] Yu Zhang,et al. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces , 2018, Expert Syst. Appl..
[18] Jiawei Han,et al. Data Mining: Concepts and Techniques , 2000 .
[19] Amedeo D'Angiulli,et al. Megacities air pollution problems: Mexico City Metropolitan Area critical issues on the central nervous system pediatric impact. , 2015, Environmental research.
[20] Jaakko Astola,et al. The class of generalized hampel filters , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).
[21] Guilherme Palermo Coelho,et al. Online Sequential Learning Based on Extreme Learning Machines for Particulate Matter Forecasting , 2017, 2017 Brazilian Conference on Intelligent Systems (BRACIS).
[22] Zhijie Zhu,et al. Research and application of a novel hybrid air quality early-warning system: A case study in China. , 2018, The Science of the total environment.
[23] João Gama,et al. Adaptive Model Rules From High-Speed Data Streams , 2014, BigMine.
[24] Xiao Feng,et al. Prediction of hourly ground-level PM2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China , 2017 .
[25] Enrico Zio,et al. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID , 2016 .
[26] On the development of an intelligent system for particulate matter air pollution monitoring, analysis and forecasting in urban regions , 2015, 2015 19th International Conference on System Theory, Control and Computing (ICSTCC).
[27] Adriano Lorena Inácio de Oliveira,et al. An approach to handle concept drift in financial time series based on Extreme Learning Machines and explicit Drift Detection , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[28] Narasimhan Sundararajan,et al. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.
[29] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[30] F. Dominici,et al. Time-series studies of particulate matter. , 2004, Annual review of public health.
[31] João Gama,et al. Learning with Drift Detection , 2004, SBIA.
[32] Eros Pasero,et al. Data-driven models to forecast PM10 concentration , 2007, 2007 International Joint Conference on Neural Networks.
[33] P. Khillare,et al. Atmospheric Particulate Matter Variations and Comparison of Two Forecasting Models for Two Indian Megacities , 2019, Aerosol Science and Engineering.