deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques … novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross …-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method …

evaluated across multiple curated feature sets generated through statistical filtering, stepwise selection, and permutation … reduced four-feature Random Forest model (car age, net premium, sum insured, and car group) achieves the highest predictive … performance (AUC = 99.62%; F1 = 98.15%), outperforming full-feature models while maintaining superior computational efficiency …

With the extensive applicability of machine learning classification algorithms to a wide spectrum of domains, feature …

has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed … analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications … a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market …