Robust logistic regression modelling via the elastic net-type regularization and tuning parameter selection

Park, Heewon; Konishi, Satoshi

doi:10.1080/00949655.2015.1073290

Cited by 19 publications

(10 citation statements)

References 21 publications

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“…The groundwater well data used in this study were collected from data obtained through field observations and measurements by Busan Metropolitan City, the groundwater basic survey report, National Groundwater Information Center (gims.go.kr, accessed on 28 January 2020), and Korea Rural Community Corporation. The collected groundwater well data were randomly classified into 153 total datasets, 70% of which were used as the model training datasets (107); the remaining 30% were used as the model validation datasets (46). Figure 1 shows the locations of the groundwater well data used in this study.…”

Section: Well Datamentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

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Section: Well Datamentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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“…We can find from Equation (2.2) that l p (β) is based on the maximum likelihood method, which is very sensitive to outliers. To obtain robust variable selection, Park and Konishi [24] proposed a weighted penalized log-likelihood function…”

Section: Review Some Classical Methodsmentioning

confidence: 99%

“…where m = ⌊αn⌋, P (S = 1) = P (S = −1) = 0.5. We set α = 0.1 and α = 0.2 to compare our proposed method (ARVSP) with the method (WRVSP) proposed by [24] and the penalized maximum likelihood (PML) method.…”

Section: D2mentioning

confidence: 99%

Robust variable selection in the logistic regression model

Jiang¹,

Zhang²,

MEİLAN³

2021

Hacettepe Journal of Mathematics and Statistics

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In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model. The proposed method is robust to outliers and considers the goodness-offit of the regression model. Furthermore, we apply an MM algorithm to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed method. The results show that when there are outliers in the dataset or the distribution of covariate variable deviates from the normal distribution, the finitesample performance of the proposed method is better than that of other existing methods. Finally, the proposed methodology is applied to the data analysis of Parkinson's disease.

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“…The L 1 -norm part of the penalty generates a sparse model by shrinking some regression coefficients exactly to zero. The L 2 -norm part of the penalty removes the limitation on the number of selected variables, encourages grouping effect, and stabilizes the L 1 regularization path[ 19 ]. An efficient algorithm LARS-EN [ 15 ] is proposed to compute the entire Enet regularization paths with the computational effort of a single OLS fit.…”

Section: Proposed Variable Selection Methodsmentioning

confidence: 99%

An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data

Liu

2017

PLoS ONE

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Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.

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Robust logistic regression modelling via the elastic net-type regularization and tuning parameter selection

Cited by 19 publications

References 21 publications

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Robust variable selection in the logistic regression model

An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data

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