Learning decision trees from random examples

Abstract

AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decision trees of rank at most r on n Boolean variables is learnable from random examples in time polynomial in n and linear in 1/ɛ and log(1/δ), where ɛ is the accuracy parameter and δ is the confidence parameter. Using a suitable encoding of variables, Rivest's polynomial learnability result for decision lists can be interpreted as a special case of this result for rank 1. As another corollary, we show that decision trees on n Boolean variables of size polynomial in n are learnable from random examples in time linear in nO(logn), 1/ɛ, and log(1/δ). As a third corollary, we show that Boolean functions that have polynomial size DNF expressions for both their positive and their negative instances are learnable from random examples in time linear in nO((logn)2), 1/ɛ, and log(1/δ)