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arXiv 2003.04952cs.LO
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Interval Temporal Logic Decision Tree Learning

Andrea Brunello, Guido Sciavicco, Ionel Eduard Stan

英文摘要:

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.

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。

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