An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment

Kiangala, Kahiomba Sonia; Wang, Zenghui

doi:10.1016/j.mlwa.2021.100024

Cited by 62 publications

(34 citation statements)

References 52 publications

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“…RF is known as an ensemble method that uses a single base learner model by combining and associating multiple decision trees. For combining trees, this algorithm normally uses the bagging or voting approach with random trees [41,42]. Ensemble learning uses different techniques such as bagging, boosting, voting, and stacking [43].…”

Section: Random Forestmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: Random Forestmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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“…Bajwa et al in [21] developed ensemble model using ResNet-152 [22], DenseNet-161 [22], SE-ResNeXt-101 [23], and NASNet [23] for the classification of seven classes of skin cancer using the ISIC dataset and achieved an accuracy of 93%. The ensemble is a machine learning method that combines the decision of several individual learners to increase classification accuracy [24]. The ensemble model exploits the diversity of individual models to make a combined decision; therefore, it is expected that the ensemble model increases classification accuracy [25,26].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

et al. 2021

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Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.

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“…Extreme Gradient Boosting is the application of gradient boosting machines (GBM), known for their suitability to perform supervised learning (Ibrahem et al, 2021), that uses the boosting ensemble learning algorithm principles to predict the outcomes (Kiangala & Wang, 2021). A detailed explanation is carried out in this research paper.…”

Section: Xgboost Modellingmentioning

confidence: 99%

A Remote Sensing and Machine Learning Based Framework For The Assessment of Spatiotemporal Water Quality Along The Middle Ganga Basin

Krishnaraj

Ramesh

2021

Preprint

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Exploring qualitative measures of any waterbody is as vital as quantitative analysis for the sustainability of our water resources. Thus, examining the dynamics of spatiotemporal behaviour of dominant Water Quality Parameters (WQPs) along any waterbody is indeed critical for proposing the appropriate water resource management. This study aims to create a Machine learning model for mapping the dominant optical and non-optical WQPs such as Electrical Conductivity (EC), pH, Temperature (Temp), Total Dissolved Solids (TDS), Silicon Dioxide (SiO2) and Dissolved Oxygen (DO) using satellite data. However, the association between WQPs and satellite data is strenuous to model precisely using simple regression theory. In this context, we developed remote sensing-based Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) regressor with optimized Hyperparameters to understand the spatiotemporal variations of WQPs using Landsat-8 imageries. We evaluated six years of satellite data for the geographical area covering from Ankinghat to Chopan (20 sampling stations under Central Water Commission (CWC), Middle Ganga Division (MGD) II) for characterizing the trends of dominant Physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the remote sensing reflectance for the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88- 0.98 for XGBoost and 0.72-0.97 for MLP have generated with bands B1-B4 and their ratios more consistent. Indeed, our findings recommend fewer in-situ measurements to generate reliable Landsat-8 based ML models to estimate Spatio-temporal variations of Physico-chemical and biological WQPs to facilitate better management of our waterbodies.

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An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment

Cited by 62 publications

References 52 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

A Remote Sensing and Machine Learning Based Framework For The Assessment of Spatiotemporal Water Quality Along The Middle Ganga Basin

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