Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction algorithms have some limitations though, due to the typical strat...
Abstract. We present a logical approach to graph theoretical learning that is based on using alphabetic substitutions for modelling graph morphisms. A classi ed graph is represente...
In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a ...
This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distr...
In this paper, we address the issue of evaluating decision trees generated from training examples by a learning algorithm. We give a set of performance measures and show how some ...