Decision tree algorithms have been among the most popular algorithms for interpretable
(transparent) machine learning since the early 1980's. The problem that has plagued …

Sparse decision tree optimization has been one of the most fundamental problems in AI
since its inception and is a challenge at the core of interpretable machine learning. Sparse …

Decision tree optimization is notoriously difficult from a computational perspective but
essential for the field of interpretable machine learning. Despite efforts over the past 40 …

Decision tree learning is a widely used approach in machine learning, favoured in
applications that require concise and interpretable models. Heuristic methods are …

Learning a decision tree from data is a difficult optimization problem. The most widespread
algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure …

In any given machine learning problem, there may be many models that could explain the
data almost equally well. However, most learning algorithms return only one of these …

Decision tree induction techniques attempt to find small trees that fit a training set of data.
This preference for smaller trees, which provides a learning bias, is often justified as being …

Inferring a decision tree from a given dataset is a classic problem in machine learning. This
problem consists of building, from a labelled dataset, a tree where each node corresponds …

Predicting nearly as well as the best pruning of a decision tree Page 1 Predicting nearly as well
as the best pruning of a decision tree David P. Helmbold Computer and Information Sciences …

The past decade has seen a significant interest on the problem of inducing decision trees
that take account of costs of misclassification and costs of acquiring the features used for …