A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation

Liao, Zhiming; Su, Mei; Ning, Guangfu; Liu, Yonglu; Wang, Tong; Zhou, Jie

doi:10.1109/access.2020.3048714

Cited by 9 publications

(8 citation statements)

References 44 publications

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“…MLR: although ARIMA is one of the most commonly used time series models, it is more applicable to scenarios with single factor inputs [ 66 ]. The model principle is simple, as shown in Figure 4 .…”

Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

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The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.

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Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Stacking ensemble learning, because of a heterogeneous integration strategy, has the ability to increase the generalization of the model. Strong model can be generated by combining several models, and the structure of stacked ensemble learning is composed of two layers [33], [34]: base learner and meta learner. The reason can be justifiable in the real world when an important decision needs to be made.…”

Section: B Overview Of the Proposed Stk-ebm Model Architecturementioning

confidence: 99%

“…The general structure of MLP is depicted in Figure 5. The related formula is designated by [24], [25], [33].…”

Section: ) Base Learner Element Selectionmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri¹,

Pierre²

2023

IEEE Access

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Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK-EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK-EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

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“…In the food material industry, 3Dprint technology is used and random forest (RF) is integrated to improve the performance. In the food processing industry, RF and gradient boosting regression (GBR) algorithm is used to optimize pellet quality [43], in addition to the abovementioned fields, In paper industry [44], chemical industry [45], pharmaceutical manufacturing [46], plastic products [47], ferrous metal [48], non-ferrous metal [49], automobile manufacturing industry [50], transportation industry [51], through method based on EL innovation, data innovation, domain innovation to enhance and improve the existing manufacturing process has obtained the best benefits. Table 4 shows application of EL has grown by leaps and bounds.…”

Section: Application In C Categorymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

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A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation

Cited by 9 publications

References 44 publications

Bus Single-Trip Time Prediction Based on Ensemble Learning

Bus Single-Trip Time Prediction Based on Ensemble Learning

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Ensemble Learning Applications in Multiple Industries: A Review

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