Unifying decision tree induction and association based classification
Hongyan Liu, Jeffrey Xu Yu, Hongjun Lü, Jian Chen · 2003
Decision tree induction is one of the widely used classification approaches. It constructs a tree in which an internal node is split based on values of a selected attribute. Depending on the attribute selected at each level of the tree, a training dataset could lead to many different trees. Consequently, it is possible that an unseen case is classified into different and conflict classes using different trees. On the other band, recently developed association role based classifications are able to generate more interesting and useful rules than decision trees. However, the large number of rules without appropriate data structure brings the issue of efficiency. In this paper, we propose to unify decision tree classification with association-based classification using generalized decision trees (GDT). GDT generalizes the concept of decision tree to encode all interesting classification rules discovered based on association in a tree form. it inherits the merits of both approaches and removes their drawbacks. The data structure and related algorithms are discussed. Results of an experimental study are presented to indicate the advantages of GDT.