Individual Classifier

Individual classifiers, the building blocks of many machine learning systems, are the focus of ongoing research aimed at improving their accuracy, efficiency, and robustness. Current efforts explore diverse architectures, including neural networks (like MobileNet) and support vector machines, and investigate ensemble methods like bagging and boosting to combine multiple classifiers for enhanced performance, particularly in addressing challenges like imbalanced datasets and limited labeled data. This research is crucial for advancing various applications, from medical diagnosis (e.g., coronary artery disease prediction) to natural language processing (e.g., genre classification), where reliable and efficient classification is paramount.