一种在构建过程中评估组合性能以构造多个决策树的算法框架
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
AI总结：
提出一种同时构造并全程评估组合性能的决策树构建框架，通过迭代候选生成与择优，优于传统装袋和提升方法。
摘要：
使用决策树组合进行预测在机器学习中被认为是有效的。构造用于预测的决策树组合的典型思路是装袋（bagging）和提升（boosting）。装袋独立地构造决策树，不评估其组合性能，并在之后对它们进行平均。提升则顺序地构造决策树，每一步仅评估新决策树与固定的先前决策树的组合性能。因此，这两种方法都没有直接构造或评估用于最终预测的决策树组合。当最终预测基于决策树的组合时，在构造它们时评估该组合的适当性是自然的。在本研究中，我们提出了一种新的算法框架，该框架同时构造决策树，并在整个构造过程中评估其组合性能。我们的框架重复两个过程。在第一个过程中，我们构造新的决策树组合候选，以找到合适的决策树组合。在第二个过程中，我们在某些标准下评估每个决策树组合的性能，并选择一个更好的组合。为了确认所提出框架的性能，我们在合成数据和基准数据上进行了实验。
英文摘要：
Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are bagging and boosting. Bagging independently constructs decision trees without evaluating their combination performance and averages them afterward. Boosting constructs decision trees sequentially, only evaluating a combination performance of a new decision tree and the fixed past decision trees at each step. Therefore, neither method directly constructs nor evaluates a combination of decision trees for the final prediction. When the final prediction is based on a combination of decision trees, it is natural to evaluate the appropriateness of the combination when constructing them. In this study, we propose a new algorithmic framework that constructs decision trees simultaneously and evaluates their combination performance throughout the construction process. Our framework repeats two procedures. In the first procedure, we construct new candidates of combinations of decision trees to find a proper combination of decision trees. In the second procedure, we evaluate each combination performance of decision trees under some criteria and select a better combination. To confirm the performance of the proposed framework, we perform experiments on synthetic and benchmark data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
