On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
This work addresses the need for scalable and flexible uncertainty estimation in black-box optimization, offering an incremental improvement over prior tree-based methods.
The paper tackled the problem of uncertainty estimation in tree-based surrogate models for sequential model-based optimization, proposing a new ensemble method called BwO forest that showed improved performance over existing tree-based models in experiments.
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.