Learning Attributional Ruletrees

Attributional ruletrees are shallow decision-tree-like structures whose leaves are sets of attributional rules or groups of classes. By using attributional ruletrees one is able in some cases to improve computational efficiency of inductive learning and understandability of generated hypotheses. A comparison of results of creating attributional ruletrees, attributional rules, and decision trees for a well-known classification problem indicates advantages of this approach.