ID3-SD: an algorithm for learning characteristic decision trees by controlling the degree of generalization
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. In this report, twomethods of reducing this problemby le