论文标题
决策树学习的全球评估
Global Evaluation for Decision Tree Learning
论文作者
论文摘要
我们将聚类的距离转移到决策树的构建过程中，因此，我们将经典的ID3算法扩展到基于树与地面真理的全球距离进行修改，而不是考虑单一叶子。接下来，我们将这个想法与原始版本相比，并讨论发生的问题，还要讨论全球方法的优势。在此基础上，我们通过确定全球评估值得的其他情况来结束。
We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves. Next, we evaluate this idea in comparison with the original version and discuss occurring problems, but also strengths of the global approach. On this basis, we finish by identifying other scenarios where global evaluations are worthwhile.
