Beyond random and greedy diversity-based sampling, several other instance selection methods can be explored to enhance the generalization of AAS models:
Algorithm Performance-Based Selection: Instances can be selected based on the historical performance of algorithms on similar problems. By considering the strengths and weaknesses of algorithms on specific problem characteristics, the training set can be tailored to improve the model's predictive accuracy.
Hybrid Sampling Strategies: Combining multiple sampling techniques, such as random, diversity-based, and performance-based sampling, can provide a more comprehensive representation of the problem space. Hybrid strategies can leverage the benefits of each method to create a more robust training set.
Active Learning: Incorporating active learning techniques allows the model to interactively select instances that are most informative for improving its performance. By focusing on instances that are most uncertain or challenging, the model can adapt and learn more effectively.
Meta-Learning: Utilizing meta-learning approaches, where the model learns how to learn from different instances, can enhance the generalization ability of AAS models. By capturing patterns and relationships across instances, meta-learning can improve the model's adaptability to new problem distributions.