论文标题
带有假设的决策树的贪婪算法
Greedy Algorithms for Decision Trees with Hypotheses
论文作者
论文摘要
我们在决策树上研究基于一个属性和查询的两个传统查询，并基于关于所有属性值的假设。这样的决策树类似于允许成员资格和等效查询的精确学习中研究的树。我们根据各种不确定性度量来构建上述决策树，并讨论来自UCI ML存储库和随机生成的布尔功能的各种数据集的计算机实验结果。我们还研究了从贪婪算法构建的决策者得出的决策规则的长度和覆盖范围。
We investigate at decision trees that incorporate both traditional queries based on one attribute and queries based on hypotheses about the values of all attributes. Such decision trees are similar to ones studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithms based on diverse uncertainty measures for construction of above decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. We also study the length and coverage of decision rules derived from the decisiontrees constructed by greedy algorithms.
