Lasso regression under stochastic restrictions in linear regression: An application to genomic data

Özkale, M. Revan

doi:10.1080/03610926.2022.2149243

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…We employed Lasso regression for feature selection to select the most predictive and relevant features to improve model performance and reduce computational burden (Bainter et al, 2023). Lasso regression compresses the coe cients of variables in the regression model by generating a penalty function to prevent over tting and address severe collinearity issues (Genç & Özkale, 2022;Guler & Guler, 2021). Lasso regression was performed with 10-fold cross-validation using the "glmnet 4.1.2" R package.…”

Section: Variable Selection and Prediction Model Establishmentmentioning

confidence: 99%

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

Chen,

Zhao,

Yang

et al. 2024

Preprint

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Background The relationship between the impact of anti-involution training on critical thinking and its propensity indicators among young healthcare professionals in dental outpatient clinics remains to be determined. Therefore, this study aimed to investigate these associations and develop an interpretable machine learning (ML) model to assess their predictive value in enhancing critical thinking through anti-involution training. Methods A cross-sectional survey encompassing 114 participants was conducted. Spearman correlation analysis was utilized to evaluate the association between propensity indicators and the enhancement of critical thinking through anti-involution training. Subsequently, the data underwent normalization utilizing the “MinMaxScaler” technique, while balancing was achieved by applying the synthetic minority oversampling technique (SMOTE). Following this, predictors were identified using the most minor absolute shrinkage and selection operator (LASSO) regression. Next, diverse machine learning algorithms constructed an individual prediction model to enhance critical thinking through anti-involution training. The prediction model's performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The Shapley additive interpretation (SHAP) method was utilized to interpret the ML model. Results Truth-seeking, analytical thinking, and inquisitiveness were identified as predictive factors for enhancing critical thinking. A Random Forest algorithm-based model incorporating these variables yielded favorable results: AUC = 0.889 (95% CI: 0.839–0.937), accuracy = 0.850, sensitivity = 0.855, specificity = 0.933. Conclusion The inclinations toward truth-seeking, analytical thinking, and inquisitiveness significantly correlate with the effectiveness of anti-involution training in enhancing critical thinking. Our simplified ML-based predictive model allows for preliminary forecasting, enabling early intervention and guidance for learners facing difficulties in improving critical thinking.

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Section: Variable Selection and Prediction Model Establishmentmentioning

confidence: 99%

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

Chen,

Zhao,

Yang

et al. 2024

Preprint

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An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

Genç

2024

Neural Process Lett

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Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .

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Mechanisms of QingRe HuoXue Formula in atherosclerosis Treatment: An integrated approach using Bioinformatics, Machine Learning, and experimental validation

Zhou,

Lin,

Miao

et al. 2024

International Immunopharmacology

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Lasso regression under stochastic restrictions in linear regression: An application to genomic data

Cited by 4 publications

References 51 publications

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model

An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method

Mechanisms of QingRe HuoXue Formula in atherosclerosis Treatment: An integrated approach using Bioinformatics, Machine Learning, and experimental validation

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