解决优化算法选择中持续新增类别时的遗忘问题。
Class Incremental Learning for Algorithm Selection
- 用重放策略缓解新类别加入时旧知识遗忘。
- 在背包问题数据集上遗忘率仅约7%。
- 适合需要持续更新求解器的实时优化场景。
算法选择常用于从一组求解器中为每个实例预测最优解法。在实际应用中，实例以流的形式持续到来，同时类别标签数量也可能随下游新数据分布而增长。因此，分类模型需定期更新以纳入新求解器，同时避免对历史数据的灾难性遗忘。这在机器学习中称为类增量学习（CIL）。尽管已在机器学习领域被广泛研究，其在优化中的算法选择应用尚未被探讨。我们基于一个背包问题数据集，评估了8种持续学习方法在抵抗灾难性遗忘方面的表现。结果表明，重放类方法显著优于其他CIL方法。虽然存在遗忘现象，但损失仅为约7%，表明这些方法在流式优化场景下具有可行性。
原文摘要 · Abstract (English)
Algorithm selection is commonly used to predict the best solver from a portfolio per per-instance. In many real scenarios, instances arrive in a stream: new instances become available over time, while the number of class labels can also grow as new data distributions arrive downstream. As a result, the classification model needs to be periodically updated to reflect additional solvers without catastrophic forgetting of past data. In machine-learning (ML), this is referred to as Class Incremental Learning (CIL). While commonly addressed in ML settings, its relevance to algorithm-selection in optimisation has not been previously studied. Using a bin-packing dataset, we benchmark 8 continual learning methods with respect to their ability to withstand catastrophic forgetting. We find that rehearsal-based methods significantly outperform other CIL methods. While there is evidence of forgetting, the loss is small at around 7%. Hence, these methods appear to be a viable approach to continual learning in streaming optimisation scenarios.
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