Voting Ensemble

Voting ensemble methods combine the predictions of multiple individual machine learning models to improve overall accuracy and robustness, addressing limitations of single models in various applications. Current research focuses on optimizing ensemble architectures, including the selection and weighting of diverse base learners (e.g., transformers, BiLSTMs, XGBoost, Inception networks, and decision stumps), and exploring different voting strategies (e.g., hard voting, weighted voting, top-k voting). This approach is proving valuable across diverse fields, enhancing performance in tasks such as anomaly detection, stock price forecasting, image classification, and medical diagnosis, ultimately leading to more reliable and accurate predictions.