对比17种模型，发现时间序列分类中特征与区间模型最适合作物算法选择。
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
- 用算法短运行轨迹作为输入，构建时间序列分类器
- 在BBOB基准上验证，特征与区间模型表现最优
- 为黑箱优化中的算法选择提供可量化的模型选型指南
黑箱优化领域中，训练算法选择器的最新方法主张采用以算法为中心的训练数据，以捕捉算法在特定实例上的表现信息，而非依赖实例特征。近期，由算法短运行过程中每轮评估的客观性能构成的探测轨迹（probing trajectories）在训练高精度选择器方面展现出显著潜力。然而，针对此类序列数据训练模型时，需选择合适的分类器。目前尚无明确指导原则来决定最适合此类时间序列数据的分类器。为此，本研究在BBOB基准套件上，对17种不同分类器和三种轨迹类型进行了大规模基准测试，采用留一实例和留一问题交叉验证。与以往基于表格数据的研究不同，本研究发现分类器的选择具有显著影响，其中特征型与区间型模型表现最佳。
原文摘要 · Abstract (English)
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate information about how an algorithm performs on an instance, rather than relying on information derived from features of the instance itself. Probing-trajectories that consist of a sequence of objective performance per function evaluation obtained from a short run of an algorithm have recently shown particular promise in training accurate selectors. However, training models on this type of data requires an appropriately chosen classifier given the sequential nature of the data. There are currently no clear guidelines for choosing the most appropriate classifier for algorithm selection using time-series data from the plethora of models available. To address this, we conduct a large benchmark study using 17 different classifiers and three types of trajectory on a classification task using the BBOB benchmark suite using both leave-one-instance out and leave-one-problem out cross-validation. In contrast to previous studies using tabular data, we find that the choice of classifier has a significant impact, showing that feature-based and interval-based models are the best choices.
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