Encoding Scheme

Encoding schemes, methods for representing data in a format suitable for machine learning algorithms, are crucial for model performance and fairness. Current research focuses on optimizing encoding for various data types (text, tabular, images), exploring techniques like one-hot encoding, similarity-based encoding, and morphology-driven byte encoding, and evaluating their impact on model accuracy and bias mitigation across different architectures (transformers, linear models). These advancements are vital for improving the efficiency and fairness of machine learning applications, particularly in areas like natural language processing, multilingual modeling, and federated learning. The development of robust and efficient encoding methods is essential for advancing the field and ensuring responsible deployment of machine learning systems.