ADASYN can be used to handle data imbalance by creating synthetic samples of the minority class and improve model performance. Really?

A 16-year-old girl, identified by loved ones as Adasyn Wood Savin, died in the crash, as did a 38-year-old family friend

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tuned XGBoost paired with SMOTE (Tuned_XGB_SMOTE) consistently achieves the highest F1 score and robust performance across all imbalance levels. SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores the impact of data imbalance, with MCC, Kappa, and F1 scores fluctuating significantly, whereas ROC AUC and PR AUC remained relatively stable. Moreover, rigorous statistical analyses employing the Friedman test and Nemenyi post hoc comparisons confirmed that the observed improvements in F1 score, PR-AUC, Kappa, and MCC were statistically significant (p < 0.05), with Tuned_XGB_SMOTE significantly outperforming Tuned_RF_GNUS. While differences in ROC-AUC were not significant, the consistency of these results across multiple performance metrics underscores the reliability of our framework, offering a statistically validated and attractive solution for model selection in imbalanced classification scenarios.

Adasyn Wood Savin, 16, was one of two people who died when a quad bike crashed into a utility truck in Tasmania on Saturday.

Background Anastomotic strictures (AS) frequently occurs in patients following esophageal cancer surgery, significantly affecting their long-term quality of life. This study aims to develop a machine learning model to predict high-risk AS, enabling early intervention and precise management. Methods A total of 1549 patients underwent radical esophageal cancer surgery and were split into a training set (1084) and a validation set (465). Adaptive Synthetic Sampling (ADASYN) handled class imbalance, while Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation refined key features. High-correlation features (r > 0.8) were assessed using variance inflation factors (VIFs) and clinical relevance. Machine learning models were trained and evaluated using area under curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA). Shapley Additive exPlanations (SHAP) analysis improved model interpretability. Results Seven critical variables were finalized, including anastomotic leakage (AL), neoadjuvant therapy (NCRT), suture method (SM), endoscopic assistance (EA), white blood cell count (WBC), albumin (Alb), and Suture site (SS). The Gradient Boosting Machine (GBM) model achieved the highest AUC, with 0.886 in the training set and 0.872 in the validation set. Shapley Additive Explanations (SHAP) analysis indicated that AL, SM, and NCRT were the most significant variables for model predictions. Conclusion The GBM machine learning model constructed in this study can effectively identify high-risk patients for AS following esophageal cancer surgery, offering strong support for earlier postoperative detection and precise clinical management. Graphical abstract

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tuned XGBoost paired with SMOTE (Tuned_XGB_SMOTE) consistently achieves the highest F1 score and robust performance across all imbalance levels. SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores the impact of data imbalance, with MCC, Kappa, and F1 scores fluctuating significantly, whereas ROC AUC and PR AUC remained relatively stable. Moreover, rigorous statistical analyses employing the Friedman test and Nemenyi post hoc comparisons confirmed that the observed improvements in F1 score, PR-AUC, Kappa, and MCC were statistically significant (p < 0.05), with Tuned_XGB_SMOTE significantly outperforming Tuned_RF_GNUS. While differences in ROC-AUC were not significant, the consistency of these results across multiple performance metrics underscores the reliability of our framework, offering a statistically validated and attractive solution for model selection in imbalanced classification scenarios.

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tuned XGBoost paired with SMOTE (Tuned_XGB_SMOTE) consistently achieves the highest F1 score and robust performance across all imbalance levels. SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores the impact of data imbalance, with MCC, Kappa, and F1 scores fluctuating significantly, whereas ROC AUC and PR AUC remained relatively stable. Moreover, rigorous statistical analyses employing the Friedman test and Nemenyi post hoc comparisons confirmed that the observed improvements in F1 score, PR-AUC, Kappa, and MCC were statistically significant (p < 0.05), with Tuned_XGB_SMOTE significantly outperforming Tuned_RF_GNUS. While differences in ROC-AUC were not significant, the consistency of these results across multiple performance metrics underscores the reliability of our framework, offering a statistically validated and attractive solution for model selection in imbalanced classification scenarios.

Abstract Imbalanced data often leads to biased models favoring the majority class, limiting the representation of the training data and complicating generalization to minority classes. Previous studies have addressed this problem by rebalancing the…

A 16-year-old girl, identified by loved ones as Adasyn Wood Savin, died in the crash, as did a 38-year-old family friend

This paper presents a malware detection system where exceptional accuracy is achieved, and class imbalance is effectively addressed using ADASYN.

"Adasyn has now been reunited with her brother Cooper Savin, together again, watching over their family from the stars," the fundraiser reads.

A 16-year-old girl, identified by loved ones as Adasyn Wood Savin, died in the crash, as did a 38-year-old family friend