Ensemble learning with imbalanced data handling in the early detection of capital markets
Putri Auliana Rifqi Mukhlashin,
Anwar Fitrianto,
Agus M Soleh
et al.
Abstract:Research aims: This study aims to create an early detection model to predict events in the Indonesian capital market.Design/Methodology/Approach: A quantitative study comparing ensemble learning models with imbalanced data handling detected early capital market events. This study used five ensemble learning models—Random Forest, ExtraTrees, CatBoost, XGBoost, and LightGBM—to detect early events in the Indonesian capital market by handling imbalanced data, such as under sampling (RUS), oversampling (SMOTE, SMOT… Show more
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