Research on Decision Forest Learning Algorithm

Abstract

Decision Forests are investigated for their ability to provide insight into the confidence associated with each prediction, the ensembles increase predictive accuracy over the individual decision tree model established. This paper proposed a novel “bottom-top” (BT) searching strategy to learn tree structure by combining different branches with the same root, and new branches can be created to overcome overfitting phenomenon.