Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Deng, Zhenyu; Han, Te; Zheng, Hao; Zhi, Fengyao; Jiajia, Jiang; Duan, Fajie

doi:10.1109/jsen.2022.3153654

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Feature engineering aims to select key variables from the original dataset according to a series of engineering methods to improve the training effect of the model (Dai et al, 2020; O’Farrell et al, 2017). Optimized variables can improve the performance of the model and algorithm (Deng et al, 2022; Sanchez-Oro et al, 2016). Works of feature engineering included data pre-processing and variable screening in this study.…”

Section: Methodsmentioning

confidence: 99%

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

Zhao,

Tao,

et al. 2023

Waste Manag Res

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Prediction of municipal solid waste (MSW) generation plays an essential role in effective waste management. The main objectives of this study were to develop models for accurate prediction of MSW generation (MSWG) and analyze the influence of dominant variables on MSWG. To elevate the model’s prediction accuracy, more than 50 municipal variables were considered original variables, which were selected from 12 categories. According to the screening results, the dominant variables are classified into four categories: urban greening, population size and residential density, regional economic development and resident income and expenditure. Among the seven machine learning methods, back propagation (BP) neural network has the best model evaluation effect. The R2 of the BP neural network model of Jiangsu, Zhejiang and Shandong provinces were 0.969, 0.941 and 0.971 respectively. The prediction accuracy of Shandong province (93.8%) was the best, followed by Jiangsu province (92.3%) and Zhejiang province (72.7%). The correlation between dominant variables and the MSWG was mined, suggesting that regional GDP and the total retail sales of consumer goods were the most important dominant variables affecting MSWG. Moreover, the MSWG might not absolutely associate with the population size and residential density. The method used in this study is a practical tool for policymakers on regional/local waste management and MSWG control.

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Section: Methodsmentioning

confidence: 99%

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

Zhao,

Tao,

et al. 2023

Waste Manag Res

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Data-Driven Sensor Fault Diagnosis Under Closed-Loop Control With Slow Feature Analysis

2022

IEEE Sensors J.

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An Extending Interclass Distance Real-Time Network Using Positional Orientation Transformation for Few-Shot Strip Steel Surface Defect Classification

Zhang,

Liu,

Guo

et al. 2024

IEEE Sensors J.

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Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Cited by 3 publications

References 34 publications

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

Data-Driven Sensor Fault Diagnosis Under Closed-Loop Control With Slow Feature Analysis

An Extending Interclass Distance Real-Time Network Using Positional Orientation Transformation for Few-Shot Strip Steel Surface Defect Classification

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