Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs

Kalule, Ramanzani; Abderrahmane, Hamid Ait; Sassi, Mohamed

doi:10.1038/s41598-023-36096-2

Cited by 26 publications

(10 citation statements)

References 49 publications

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“…To be clear, the meta-classifier learns from the predictions made by the base classifiers rather than the original input data [ 32 , 33 ]. The goal of the meta-learner is to combine these predictions effectively, considering the strengths and weaknesses of each base classifier, in order to make the final ensemble classification [ 34 ]. Stacking delivers superior performance compared to boosting and bagging when it is maximumly optimized [ 35 ].…”

Section: Review Of Literaturementioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

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Section: Review Of Literaturementioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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“…The application of meta-learning in ensemble models for tasks like time series forecasting, energy consumption prediction, and network traffic classification has shown promising outcomes in enhancing model generalizability and robustness [ 57 ]. The stacking ensemble approach, which consolidates predictions from multiple machine learning models into a single meta-learner model, has proven particularly effective in accelerating predictions and enhancing overall performance [ 58 ]. By utilizing meta-learning techniques in ensemble models, researchers can achieve improved performance by amalgamating predictions from multiple models, ultimately leading to more robust and accurate outcomes across a wide array of applications [ 59 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

Alalhareth,

Hong

2024

Sensors

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This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.

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“…It outperforms conventional inversion methods and traditional machine learning for porosity inference in handling these challenging scenarios. Neural networks that make DL models can be scaled up to adapt to diverse scenarios, including multi-output regressions, resulting in enhanced performance (Feng et al, 2020;Kalule et al, 2023).…”

Section: Deep Learning Inversionmentioning

confidence: 99%

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ntibahanana,

Jianguo,

Luemba

et al. 2024

Earth and Space Science

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Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

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Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs

Cited by 26 publications

References 49 publications

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

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