Improving accuracy models using elastic net regression approach based on empirical mode decomposition

Al-Jawarneh, Abdullah S.; Ismail, Mohd Tahir; Awajan, Ahmad M.; Alsayed, Ahmed R. M.

doi:10.1080/03610918.2020.1728319

Cited by 14 publications

(5 citation statements)

References 28 publications

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“…The response variable is simulated according to one or two components from the predictor variables. This notion is in line with the simulation study proposed by (Qin et al, 2016;Al-Jawarneh et al, 2020) for generating variables.…”

Section: Numerical Experimentssupporting

confidence: 85%

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Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Al-Jawarneh

Ismail

Awajan

2021

Int J Math, Eng, Manag Sci

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Elastic net (ELNET) regression is a hybrid statistical technique used for regularizing and selecting necessary predictor variables that have a strong effect on the response variable and deal with multicollinearity problem when it exists between the predictor variables. The empirical mode decomposition (EMD) algorithm is used to decompose the nonstationary and nonlinear dataset into a finite set of orthogonal intrinsic mode function components and one residual component. This study mainly aims to apply the proposed ELNET-EMD method to determine the effect of the decomposition components of multivariate time-series predictors on the response variable and tackle the multicollinearity between the decomposition components to enhance the prediction accuracy for building a fitting model. A numerical experiment and a real data application are applied. Results show that the proposed ELNET-EMD method outperforms other existing methods by capable of identifying the decomposition components that have the most significance on the response variable despite the high correlation between the decomposition components and by improving the prediction accuracy.

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Section: Numerical Experimentssupporting

confidence: 85%

“…LASSO regression based on decomposition components via EMD method (Qin et al, 2016). ELNET regression based on EMD method proposed to deal with nonlinear and non-stationary original univariate time-series predictor case (Al-Jawarneh et al, 2020). Kernel Ridge regression based on the decomposition components via EMD (Naik et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Al-Jawarneh

Ismail

Awajan

2021

Int J Math, Eng, Manag Sci

Self Cite

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“…This was regarded as an improved form of multiple linear regression using ordinary least squares [65]. Recent studies demonstrated superior performance in using ENet over other regression methods in handling multicollinearity of predictors for numerical predictions [66,67].…”

Section: The Use Of Machine Learningmentioning

confidence: 99%

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

2021

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Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature.

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“…In addition, for higher accuracy estimation and to minimize the mean-squared prediction error, we tuned the parameters α and λ through 5-fold cross-validation. [75,76]. Data division was random and performed automatically by the software.…”

Section: Methodsmentioning

confidence: 99%

Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Debone

Martins

Miraglia

2022

Climate

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Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.

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Improving accuracy models using elastic net regression approach based on empirical mode decomposition

Cited by 14 publications

References 28 publications

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques

Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

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