  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.
