Forest Pruning Based on Branch Importance. (1st June 2017)

• Record Type: Journal Article
• Authors: Jiang, Xiangkui; Wu, Chang-an; Guo, Huaping
• Other Names: Schmuker Michael Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2017(2017)
• Publication Date: 2017-06-01
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2017/3162571
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.