Tag: SMOTE

In a single-center retrospective study of 240 pyogenic spondylitis patients, a random forest model using six routine early indicators accurately ...

Researchers evaluated hierarchical and flat machine learning models, finding a two-stage approach cut memory use while exceeding 99 percent detection ...

Researchers developed a long short-term memory intrusion detection framework that achieved 92 percent accuracy on network traffic after utilizing generative ...

An integrated machine-learning framework using XGBoost achieved 99.94 percent accuracy in classifying sorghum yields and modeled potential conflict-related crop losses ...

An explainable machine learning pipeline combining XGBoost and SHAP revealed that specific patient knowledge deficits were strongly associated with tuberculosis ...

A study reveals that artificial data balancing techniques distort probabilistic crash risk predictions and heavily overestimate traffic fatality hazards in ...

An Extreme Gradient Boosting model using routinely collected bedside data accurately predicted delirium in non-mechanically ventilated sepsis patients during internal ...

A new deep learning method using Gaussian features achieves 97.93% accuracy in ECG heartbeat classification on the MIT–BIH Arrhythmia Database.

An interpretable machine learning framework combining Schrödinger optimization and SHAP analysis improved four-class rockburst intensity prediction and passed preliminary validation ...

A newly published study shows that StatMatch, a statistical matching-based undersampling method, can match SMOTE on some imbalanced datasets although ...