With the extensive applicability of machine learning classification algorithms to a wide spectrum of domains, feature … selection (FS) becomes a relevant data preprocessing technique due to the high dimensionality of data used in these domains …

explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression …, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection methods, including SHapley … SHAP-based feature selection with SMOTE-Tomek resampling and a Stacked Classifier consistently achieves superior predictive …

Insurance Claim Fraud Detection (AICFD) approach. The feature selection process in the proposed approach uses Golden Eagle …

method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and …

detection depends on identifying the most critical features. Traditional feature selection (FS) methods often struggle to …

This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information … the efficacy of addressing feature selection. The information gain, random forest, and relief F-based approach are used to … different multi-objective feature selection techniques on five different high-dimensional datasets. Our proposed model …

Algorithms (GA) for feature selection with LightGBM for classification. The GA component identifies the most relevant features …