Score Level Fusion

Score-level fusion is a machine learning technique that combines the outputs of multiple models to improve prediction accuracy and robustness. Current research focuses on applying this method across diverse domains, including medical image analysis, speaker verification, and educational technology, often employing algorithms like linear logistic regression, weighted averaging, and neural networks (e.g., multi-layer perceptrons) to integrate individual model scores. This approach is particularly valuable when dealing with noisy data, heterogeneous sources, or imbalanced classes, leading to improved performance in applications ranging from disease diagnosis to biometric authentication. The effectiveness of score-level fusion hinges on careful consideration of score calibration and the selection of appropriate fusion strategies to maximize the complementary strengths of individual models.