A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs

Park, U-Yeol; Kang, Young‐Hoon; Lee, Haneul; Yun, Seok-Heon

doi:10.3390/app12199729

Cited by 27 publications

(11 citation statements)

References 27 publications

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“…Stacking is an ensemble method that fuses multiple classification models consisting of base and meta-classifiers [ 29 ]. This method unifies various machine-learning methods and shares similarities with other ensemble approaches, such as bagging and boosting [ 30 ]. Stacking’s architecture is divided into two stages: level 0 and level 1 [ 31 ].…”

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 study aims to demonstrate how managers can benefit more from the data of completed projects by using ML. Similarly, Park et al [24] proposes a two-level stacking heterogeneous ensemble algorithm that combines RF, SVM, and CatBoosting to estimate the cost of building construction projects at an early stage. The proposed method was evaluated using cost information data disclosed by the public procurement service in South Korea.…”

Section: Related Workmentioning

confidence: 99%

Predicting the outcome of regional development projects using machine learning

Satri,

El Mokhi,

Hachimi

2024

IJ-AI

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<p><span lang="EN-US">Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.</span></p>

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“…We employed the Boruta feature selection algorithm to rst identify the most in uential predictor variables, and subsequently aggregated the predictive power of these variables through a stacking ensemble that incorporates multiple ML methods. Stacking is a powerful ML technique because the use of various models can complement each other on di cult to predict samples, and the ensemble of models can often outperform any individual model [23][24][25][26] . To our knowledge, our group is the rst to implement the stacking methodology to radiation biodosimetry 7 .…”

Section: Introductionmentioning

confidence: 99%

Validation of protein biomarker panel in peripheral blood leukocytes of juvenile and adult C57BL/6 mice up to seven days post-exposure for machine learning-based radiation biodosimetry

Nemzow,

Phillippi,

Kanagaraj

et al. 2024

Preprint

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Following a large-scale radiological event, timely collection of samples from all potentially exposed individuals may be precluded, and high-throughput bioassays capable of rapid and individualized dose assessment several days post-exposure will be essential for population triage and efficient implementation of medical treatment. The objective of this work was to validate the performance of a biomarker panel of radiosensitive intracellular leukocyte proteins (ACTN1, DDB2, and FDXR) and blood cell counts (CD19 + B cells and CD3 + T-cells) for retrospective classification of exposure and dose estimation up to 7 days post-exposure in an in vivo C57BL/6 mouse model. Juvenile and adult C57BL/6 mice of both sexes were total body irradiated with 0, 1, 2, 3, or 4 Gy, peripheral blood was collected 1, 4, and 7-days post-exposure, and individual blood biomarkers were quantified by Imaging Flow Cytometry. An ensemble machine learning platform was used to identify the strongest predictor variables and combine them for biodosimetry outputs. This approach generated successful exposure classification (ROC AUC = 0.94, 95% CI: 0.90–0.97) and quantitative dose reconstruction (R2 = 0.79, RMSE = 0.68 Gy, MAE = 0.53 Gy), supporting the potential utility of the proposed biomarker assay for determining exposure and received dose in an individual.

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A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs

Cited by 27 publications

References 27 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

Predicting the outcome of regional development projects using machine learning

Validation of protein biomarker panel in peripheral blood leukocytes of juvenile and adult C57BL/6 mice up to seven days post-exposure for machine learning-based radiation biodosimetry

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