Interval Temporal Logic Decision Tree Learning

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...

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Bibliographic Details
Published inLogics in Artificial Intelligence pp. 778 - 793
Main Authors Brunello, Andrea, Sciavicco, Guido, Stan, Ionel Eduard
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Decision trees
Interval temporal logics
Symbolic learning
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Summary: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.
ISBN:3030195694
9783030195694
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-19570-0_50