Feature Name

Feature selection and extraction are crucial for improving machine learning model performance, particularly when dealing with high-dimensional or heterogeneous data. Current research focuses on developing efficient algorithms, such as loop improvement methods for federated learning and novel fusion frameworks combining handcrafted and deep learning features, to optimize feature representation and reduce computational complexity. These advancements are impacting diverse fields, from sentiment analysis and defect prediction to multi-source domain adaptation and clinical data analysis, by enhancing model accuracy, interpretability, and generalizability. The ultimate goal is to identify and utilize the most informative features, leading to more robust and reliable machine learning models across various applications.