Comparative Evaluation
Comparative evaluation assesses the relative performance of different methods, models, or algorithms across various tasks and datasets, aiming to identify optimal approaches for specific applications. Current research focuses on efficient comparative assessment techniques, particularly for large language models (LLMs) and deep learning architectures, often employing methods like pairwise comparisons and metaheuristic optimization to handle computational complexity and hyperparameter tuning. This field is crucial for advancing numerous disciplines, from automated essay scoring and weather forecasting to medical image analysis and robotics, by providing rigorous benchmarks and informing the selection of the most effective tools for diverse applications.
Papers
Comparative Evaluation of Applicability Domain Definition Methods for Regression Models
Shakir Khurshid, Bharath Kumar Loganathan, Matthieu Duvinage
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors
Valentina Vadori, Antonella Peruffo, Jean-Marie Graïc, Livio Finos, Enrico Grisan