On the Robustness of Decision Tree Learning Under Label Noise

In most practical problems of classifier learning, the training data suffers from label noise. Most theoretical results on robustness to label noise involve either estimation of noise rates or non-convex optimization. Further, none of these results are applicable to standard decision tree learning algorithms. This paper presents some theoretical analysis to show that, under some assumptions, many popular decision tree learning algorithms are inherently robust to label noise. We also present some sample complexity results which provide some bounds on the sample size for the robustness to hold with a high probability. Through extensive simulations we illustrate this robustness.