Multiple Classifier

Multiple classifier systems (MCS), also known as ensemble learning, aim to improve prediction accuracy and robustness by combining the outputs of multiple individual classifiers. Current research focuses on optimizing ensemble selection strategies, including dynamic selection methods and pruning techniques to reduce computational cost, and exploring the use of diverse base classifiers (e.g., neural networks, support vector machines, decision trees) within ensembles for various applications. The effectiveness of MCS is demonstrated across diverse fields, from biosignal processing and natural language processing to image classification and credit risk assessment, highlighting their significance for improving the performance and reliability of machine learning models in practical settings.