Dynamic Trees for Learning and Design
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用于学习与试验设计的动态树
- 研究问题
- 在数据逐步到达的在线环境中，如何让回归或分类树随新信息更新，并高效表达复杂响应面及预测不确定性？
- 方法与思路
- 作者构造随数据积累变化的序贯树模型，以粒子学习过滤树状态；叶节点采用常数、线性或多项模型，并设置默认先验。
- 材料陈述的贡献
- 摘要称，该方法适用于在线分类、主动学习和优化等序贯设计，在所展示的例子中以较低成本取得更好的结果。
- 阅读关注点
- 应核对局部树变化规则、粒子更新与参数积分方式，以及预测表现、计算成本和序贯设计效果的比较标准。
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- 仅据OpenAlex索引摘要，稿件版本未注明；摘要未报告比较数值、粒子退化或性能条件，不能推广其效果和成本优势。
原文摘要
Dynamic regression trees are an attractive option for automatic regression and classification with complicated response surfaces in online application settings. We create a sequential tree model whose state changes in time with the accumulation of new data, and provide particle learning algorithms that allow for the efficient online posterior filtering of tree states. A major advantage of tree regression is that it allows for the use of very simple models within each partition. The model also facilitates a natural division of labor in our sequential particle-based inference: tree dynamics are defined through a few potential changes that are local to each newly arrived observation, while global uncertainty is captured by the ensemble of particles. We consider both constant and linear mean functions at the tree leaves, along with multinomial leaves for classification problems, and propose default prior specifications that allow for prediction to be integrated over all model parameters conditional on a given tree. Inference is illustrated in some standard nonparametric regression examples, as well as in the setting of sequential experiment design, including both active learning and optimization applications, and in online classification. We detail implementation guidelines and problem specific methodology for each of these motivating applications. Throughout, it is demonstrated that our practical approach is able to provide better results compared to commonly used methods at a fraction of the cost.