The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
While the decision tree is an effective representation that has been used in many domains, a tree can often encode a concept inefficiently. This happens when the tree has to repres...
—Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is ...
We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the dec...
Abstract. Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncert...