论文标题
间隔时间逻辑决策树学习
Interval Temporal Logic Decision Tree Learning
论文作者
论文摘要
决策树是简单但功能强大的分类模型，用于对分类和数值数据进行分类，尽管它们很简单，但它们通常在操作研究和管理以及知识挖掘中使用。从逻辑的角度来看，决策树可以看作是用命题逻辑编写的一组结构化的逻辑规则。由于知识挖掘正在迅速发展朝着时间知识挖掘发展，并且在许多情况下，时间信息可以通过间隔时间逻辑来描述，因此命题逻辑决策树可能会发展为间隔的时间逻辑决策树。在本文中，我们定义了间隔时间逻辑决策树学习的问题，并提出了一种概括经典决策树学习的解决方案。
Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in propositional logic. Since knowledge mining is rapidly evolving towards temporal knowledge mining, and since in many cases temporal information is best described by interval temporal logics, propositional logic decision trees may evolve towards interval temporal logic decision trees. In this paper, we define the problem of interval temporal logic decision tree learning, and propose a solution that generalizes classical decision tree learning.
