Label Encoding

Label encoding, the process of representing categorical data (labels) in a numerical format suitable for machine learning models, is crucial for improving the efficiency and accuracy of various prediction tasks. Current research focuses on developing effective encoding schemes for diverse applications, including multi-label classification, regression, and few-shot learning, exploring techniques like circular vectors, binary encoding, and label semantic encoding to optimize model performance and reduce computational costs. These advancements are impacting fields ranging from image processing and natural language processing to software performance prediction, enabling more robust and efficient solutions for complex problems. The development of automated label encoding learning methods further enhances the applicability and generalizability of these techniques across different datasets and model architectures.