Bagging One-Class Decision Trees

摘要

POSC4.5 is a one-class decision tree classifier with good classification accuracy which learns from both positive and unlabeled examples. In order to further improve the classification accuracy and robustness of POSC4.5, in this paper, we ensemble POSC4.5 trees by bagging, and classify testing samples by majority voting. The experiment results on 5 UCI datasets show that the classification accuracy and robustness of POSC4.5 could be improved by our approach.