To generate discriminatory training samples for algorithm selection and performance prediction tasks, other algorithms and meta-learning techniques could be explored. One potential approach could involve using reinforcement learning algorithms, such as Q-learning or deep reinforcement learning, to learn the optimal trajectory generation strategy. These algorithms could adaptively adjust the trajectory generation process based on feedback from the performance of the ML models, leading to more effective discriminatory trajectories.
Another technique to consider is genetic programming, which could be used to evolve the trajectory generation process. By evolving a set of rules or functions that define how trajectories are generated, genetic programming could optimize the trajectory generation process to produce more discriminatory samples. This approach could potentially lead to more efficient and effective trajectory generation for algorithm selection and performance prediction tasks.