Adatree 2: Boosting to build decision trees or Improving Adatree with soft splitting rules

We extend the framework of Adaboost so that it builds a smoothed decision tree rather than a neural network. The proposed method, “Adatree 2”, is derived from the assumption of a probabilistic observation model. It avoids the problem of over-fitting that appears in other tree-growing methods by reweighing the training examples, rather than splitting the training dataset at each node. It differs from Adaboost by steering the input data towards weak classifiers that are “tuned ” to the conditional probability determined by the output of previously evaluated classifiers. As a consequence, Adatree 2 enjoys a lower computation cost than Adaboost. After defining this method, we present some early experimental results and identify some issues left open for future research. 1 Boosting and decision trees Boosting [1, 2] refers to methods to build weighed combinations of “weak classifiers”. At each training round, a weak classifier is added to the existing combination. These method