Class Incremental Learning for Algorithm Selection

• URL: http://arxiv.org/abs/2506.01545v1
• Date: Mon, 02 Jun 2025 11:18:07 GMT
• Title: Class Incremental Learning for Algorithm Selection
• Authors: Mate Botond Nemeth, Emma Hart, Kevin Sim, Quentin Renau,
• Abstract summary: We study the relevance of algorithm-selection in optimisation scenarios.<n>We benchmark 8 continual learning methods with respect to their ability to withstand catastrophic forgetting.<n>We find that rehearsal-based methods significantly outperform other CIL methods.
• Score: 0.16874375111244325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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