走向可解释的探索性景观分析：用于分类BBOB函数的极端特征选择
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
摘要：
在机器学习（ML）的最新进展的推动下，优化算法的自动化设计目前正在动摇演化计算（EC）。在手工挑选的指导方针，选择一个最合适的启发式设计长期以来一直主导着该领域的研究活动，自动训练的启发式现在被认为优于人类派生的选择，即使是研究充分的优化任务。因此，基于ML的电子商务不再是未来的愿景，而是我们社会不可或缺的一部分。基于ML的逻辑学经常面临的一个关键批评是它们可能缺乏可解释性，这可能会阻碍未来的发展。这特别适用于监督学习技术，该技术基于探索性景观分析（ELA）来推断算法的性能。在这样的应用程序中，使用几十个问题特征来构建特定算法选择或配置任务的模型并不罕见。我们在这项工作中的目标是分析是否确实需要这么多功能。使用分类的BBOB测试功能作为测试床，我们表明，一个令人惊讶的小数目的功能-往往少于4-就足以达到98%的准确率。有趣的是，发现满足此阈值所需的特征数量随着问题维度的增加而减少。我们表明，分类精度转移到设置中，其中几个实例参与训练和测试。然而，在leave-one-instance-out设置中，分类精度显著下降，并且特征的变换不变性成为决定性的成功因素。
英文摘要：
Facilitated by the recent advances of Machine Learning (ML), the automated design of optimization heuristics is currently shaking up evolutionary computation (EC). Where the design of hand-picked guidelines for choosing a most suitable heuristic has long dominated research activities in the field, automatically trained heuristics are now seen to outperform human-derived choices even for well-researched optimization tasks. ML-based EC is therefore not any more a futuristic vision, but has become an integral part of our community. A key criticism that ML-based heuristics are often faced with is their potential lack of explainability, which may hinder future developments. This applies in particular to supervised learning techniques which extrapolate algorithms' performance based on exploratory landscape analysis (ELA). In such applications, it is not uncommon to use dozens of problem features to build the models underlying the specific algorithm selection or configuration task. Our goal in this work is to analyze whether this many features are indeed needed. Using the classification of the BBOB test functions as testbed, we show that a surprisingly small number of features -- often less than four -- can suffice to achieve a 98\% accuracy. Interestingly, the number of features required to meet this threshold is found to decrease with the problem dimension. We show that the classification accuracy transfers to settings in which several instances are involved in training and testing. In the leave-one-instance-out setting, however, classification accuracy drops significantly, and the transformation-invariance of the features becomes a decisive success factor.
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