Decision Level Fusion

Decision-level fusion integrates the outputs of multiple independent decision-making systems to improve overall accuracy and robustness. Current research focuses on applying this technique across diverse fields, including autonomous driving, medical diagnosis (e.g., Parkinson's disease classification), and remote sensing, often employing machine learning algorithms like convolutional neural networks (CNNs) and ensemble methods such as majority voting or weighted averaging to combine predictions. This approach is significant because it leverages the complementary strengths of different models or data sources, leading to more reliable and accurate results in applications where individual systems may be prone to errors or limitations.