Classifier Fusion

Classifier fusion aims to improve the accuracy and robustness of classification systems by combining the predictions of multiple individual classifiers. Current research focuses on optimizing fusion strategies, including exploring diverse ensemble creation methods (e.g., evolutionary algorithms), employing various model architectures (e.g., convolutional neural networks, shallow multi-label networks), and developing novel weighting schemes (e.g., Lp-norm constraints). This approach is proving valuable across diverse applications, from land-use mapping and power grid classification to ancient character recognition, demonstrating its broad applicability and potential for enhancing the performance of machine learning systems.