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A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class water quality Index Prediction

Computers, Materials & Continua 2023年第8期76卷 1361-1384页

作者： Wen Yee Wong Khairunnisa Hasikin Anis Salwa Mohd Khairuddin Sarah Abdul Razak Hanee Farzana Hizaddin Mohd Istajib Mokhtar Muhammad Mokhzaini Azizan Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Institute of Biological Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur50603Malaysia Department of Chemical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia Department of Science and Technology Studies Faculty of ScienceUniversity of MalayaKuala Lumpur50603Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering and Built EnvironmentUniversiti Sains Islam MalaysiaNilaiNegeri Sembilan71800Malaysia

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of *** are significantly more samples for day-to-day classes,while rare events such as polluted classes are ***,the limited availability of minority outcomes lowers the classifier’s overall *** study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 *** intends to balance the misled accuracy towards the majority of ***,10 ML algorithms of its performance are *** classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer *** study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning *** comparison results revealed that a highaccuracy machine learning model is not always good in recall and *** paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input *** proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,*** addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are *** experimental setup concluded XGBoost with a higher balanced accuracy and ***,the SE-DL model has a better and more balanced performance in the F1 *** SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality *** proposed algorithm is also capable of higher efficiency at a lower computa

关键词： water quality classification imbalanced data SMOTE stacked ensemble deep learning sensitivity analysis

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