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- W3100854962 abstract "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." @default.
- W3100854962 created "2020-11-23" @default.
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- W3100854962 date "2019-01-01" @default.
- W3100854962 modified "2023-09-28" @default.
- W3100854962 title "Interval Temporal Logic Decision Tree Learning" @default.
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- W3100854962 doi "https://doi.org/10.1007/978-3-030-19570-0_50" @default.
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