A novel dual-scale ensemble learning paradigm with error correction for predicting daily ozone concentration based on multi-decomposition process and intelligent algorithm optimization, and its application in heavily polluted regions of China

2021 ◽  
pp. 101306
Author(s):  
Jianguo Zhou ◽  
Zhongtian Xu ◽  
Shiguo Wang
Keyword(s):  
Error Correction ◽  
Ensemble Learning ◽  
Ozone Concentration ◽  
Decomposition Process ◽  
Algorithm Optimization ◽  
Learning Paradigm ◽  
Intelligent Algorithm ◽  
Dual Scale

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM 10 forecasting

2018 ◽  
Vol 201 ◽  
pp. 34-45 ◽  
Author(s):  
Hongyuan Luo ◽  
Deyun Wang ◽  
Chenqiang Yue ◽  
Yanling Liu ◽  
Haixiang Guo
Keyword(s):  
Error Correction ◽  
Ensemble Learning ◽  
Learning Paradigm ◽  
Pm 10 ◽  
Hybrid Decomposition

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

2012 ◽  
Vol 93 ◽  
pp. 432-443 ◽  
Author(s):  
Ling Tang ◽  
Lean Yu ◽  
Shuai Wang ◽  
Jianping Li ◽  
Shouyang Wang
Keyword(s):  
Energy Consumption ◽  
Ensemble Learning ◽  
Nuclear Energy ◽  
Learning Paradigm

A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

2010 ◽  
pp. 105-117
Author(s):  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Ensemble ◽  
Credit Risk Management ◽  
Learning Paradigm ◽  
Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

2008 ◽  
Vol 30 (5) ◽  
pp. 2623-2635 ◽  
Author(s):  
Lean Yu ◽  
Shouyang Wang ◽  
Kin Keung Lai
Keyword(s):  
Neural Network ◽  
Crude Oil ◽  
Ensemble Learning ◽  
Oil Price ◽  
Neural Network Ensemble ◽  
Crude Oil Price ◽  
Learning Paradigm

Interactive image segmentation with a regression based ensemble learning paradigm

2017 ◽  
Vol 18 (7) ◽  
pp. 1002-1020 ◽  
Author(s):  
Jin Zhang ◽  
Zhao-hui Tang ◽  
Wei-hua Gui ◽  
Qing Chen ◽  
Jin-ping Liu
Keyword(s):  
Image Segmentation ◽  
Ensemble Learning ◽  
Learning Paradigm ◽  
Interactive Image Segmentation

scholarly journals Research on a Novel Hybrid Decomposition–Ensemble Learning Paradigm Based on VMD and IWOA for PM2.5 Forecasting

2021 ◽  
Vol 18 (3) ◽  
pp. 1024
Author(s):  
Hengliang Guo ◽  
Yanling Guo ◽  
Wenyu Zhang ◽  
Xiaohui He ◽  
Zongxi Qu
Keyword(s):  
Ensemble Learning ◽  
Environmental Data ◽  
Forecasting Model ◽  
Test Cases ◽  
Learning Paradigm ◽  
Forecasting Methods ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Weight Coefficients ◽  
Hybrid Decomposition

The non-stationarity, nonlinearity and complexity of the PM2.5 series have caused difficulties in PM2.5 prediction. To improve prediction accuracy, many forecasting methods have been developed. However, these methods usually do not consider the importance of data preprocessing and have limitations only using a single forecasting model. Therefore, this paper proposed a new hybrid decomposition–ensemble learning paradigm based on variation mode decomposition (VMD) and improved whale-optimization algorithm (IWOA) to address complex nonlinear environmental data. First, the VMD is employed to decompose the PM2.5 sequences into a set of variational modes (VMs) with different frequencies. Then, an ensemble method based on four individual forecasting approaches is applied to forecast all the VMs. With regard to ensemble weight coefficients, the IWOA is applied to optimize the weight coefficients, and the final forecasting results were obtained by reconstructing the refined sequences. To verify and validate the proposed learning paradigm, four daily PM2.5 datasets collected from the Jing-Jin-Ji area of China are chosen as the test cases to conduct the empirical research. The experimental results indicated that the proposed learning paradigm has the best results in all cases and metrics.

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