We empirically compare our approaches against standard induction algorithms, showing that the decision trees we obtain can sometimes be less than half the size of those found by other greedy methods. We study alternative formulations based on satisfiability, constraint programming, and hybrids with integer linear programming. Specifically, we formulate decision tree induction as a combinatorial optimisation problem in which the objective is to minimise the number of nodes in the tree. In this paper we take this principle to the extreme.
Informally, this principle states that one should prefer the simpler hypothesis. This preference for smaller trees, which provides a learning bias, is often justified as being consistent with the principle of Occam’s Razor. Decision tree induction techniques attempt to find small trees that fit a training set of data.
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