Learning characteristic decision trees
Decision trees constructed by ID3-like algorithms suffer from an inability of detecting instances of categories not present in the set of training examples, i.e., they are discriminative representations. Instead, such instances are assigned to one of the classes actually present in the training set, resulting in undesired misclassifications. Two methods of reducing this problem by learning charact