What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?

Marcot, Bruce G.; Hanea, Anca M.

doi:10.1007/s00180-020-00999-9

Cited by 233 publications

(119 citation statements)

References 27 publications

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“…This method helped to provide information on the robustness and accuracy of the model in predicting the outcomes. The use and reliability of this method to predict model accuracy has been extensively studied in the literature, for example [ [57] , [58] , [59] , [60] ]. A database file (presented in Data-In-Brief) consists of 100 cases (n = 100) was compiled, and each case had values of the response variable (biomass feedstock gate availability) and covariates.…”

Section: Methodsmentioning

confidence: 99%

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry

Sajid

2021

Renewable and Sustainable Energy Reviews

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The novel coronavirus (COVID-19) is highly detrimental, and its death distribution peculiarity has severely affected people’s health and the operations of businesses. COVID-19 has wholly undermined the global economy, including significant damage to the ever-emerging biomass supply chain; its sustainability is disintegrating due to the coronavirus. The biomass supply chain must be sustainable and robust enough to adapt to the evolving and fluctuating risks of the market due to the coronavirus. However, no such study has been performed so far. To address this issue, investigating how COVID-19 influences a biomass supply chain is vital. This paper presents a dynamic risk assessment methodological framework to model biomass supply chain risks due to COVID-19. Using a dynamic Bayesian network (DBN) formalism, the impacts of COVID-19 on the performance of biomass supply chain risks were studied. The proposed model was applied to the biomass supply chain of a U.S.-based General Biodiesel company in Washington, USA. The case study results show that it would take one year to recover from the maximum damage to the biomass supply chain due to COVID-19, while full recovery would require five years. Results indicate that biomass feedstock gate availability (FGA) is 2% due to pandemic and lockdown conditions. Due to the availability of vaccination and gradual business reopenings, this availability increases to 92% in the second year. Results also indicate that the price of fossil-based fuel will gradually increase after one year of the pandemic; however, the market prices of fossil-based fuel will not revert to pre-coronavirus conditions even after nine years. K-fold cross-validation is used to validate DBN. Results of validation indicate a model accuracy of 95%. It is concluded that pandemic has caused risks to the sustainability of biomass feedstock, and the current study can help develop risk mitigation strategies.

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Section: Methodsmentioning

confidence: 99%

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry

Sajid

2021

Renewable and Sustainable Energy Reviews

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“…To measure the performance of the obtained Bayesian Network, a ten-fold cross-validation has been conducted. K-fold cross-validation is a powerful means of testing the success rate, accuracy, and robustness of models used for classification [ 63 ]. It consists of randomly partitioning the dataset into ten subsets of equal size; then each subset, in turn, is used to validate the model fitted on the remaining k-1 subsets.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

Herrero

Febres

Boulagouas

et al. 2021

IJERPH

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Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

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“…The maximum color shift value was 50. Table 6 shows the K-fold cross validation setting, which was used in the experiment to report unbiased performances [28]. For each trial, the training image set contained 216 DCIS and 216 HB images.…”

Section: Methodsmentioning

confidence: 99%

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Zhang

Satapathy

et al. 2020

Complex Intell. Syst.

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Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.

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What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?

Cited by 233 publications

References 27 publications

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

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