论文标题
基于顺序模型的优化，基于树的替代模型的不确定性估计
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
论文作者
论文摘要
基于顺序模型的优化通过构造具有评估历史的替代模型来顺序选择候选点，以解决黑盒优化问题。高斯过程（GP）回归是作为替代模型的流行选择，因为它在分析上计算预测不确定性的能力。另一方面，随机树的合奏是另一种选择，由于其可扩展性和处理连续/离散的混合变量的可扩展性和轻松性，因此对GPS具有实际优点。在本文中，我们在预测不确定性估计的角度重新审视了随机树的各种集合，以调查其行为。然后，我们提出了一种构建一组随机树的新方法，被称为BWO森林，在该林中使用过采样的装袋来构造自举样品，这些样本用于建造随机分裂的随机树。实验结果表明，在各种情况下，BWO森林对现有基于树的模型的有效性和良好性能。
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem. Gaussian process (GP) regression is a popular choice as a surrogate model, because of its capability of calculating prediction uncertainty analytically. On the other hand, an ensemble of randomized trees is another option and has practical merits over GPs due to its scalability and easiness of handling continuous/discrete mixed variables. In this paper we revisit various ensembles of randomized trees to investigate their behavior in the perspective of prediction uncertainty estimation. Then, we propose a new way of constructing an ensemble of randomized trees, referred to as BwO forest, where bagging with oversampling is employed to construct bootstrapped samples that are used to build randomized trees with random splitting. Experimental results demonstrate the validity and good performance of BwO forest over existing tree-based models in various circumstances.
