Refining and tuning the weights assigned to features, labels, and decisions in the decision-aware dataset distance can be achieved through a systematic approach that does not rely on transferability measures. One effective method is to employ a grid search or optimization algorithm to explore the weight space independently. By defining a range of potential weights for features, labels, and decisions, one can evaluate the performance of the dataset distance across various configurations without directly linking it to transferability outcomes.
Another approach is to utilize cross-validation techniques, where the dataset is partitioned into training and validation sets. By assessing the performance of the decision-aware dataset distance on the validation set for different weight combinations, one can identify optimal weights that maximize the predictive power of the distance measure. This process can be further enhanced by incorporating regularization techniques to prevent overfitting and ensure that the weights generalize well across different datasets.
Additionally, incorporating expert knowledge or domain-specific insights can guide the weight assignment process. By understanding the relative importance of features, labels, and decisions in the context of specific PtO tasks, practitioners can make informed decisions about weight allocation, leading to a more tailored and effective distance measure.