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Computational Intelligence and Neuroscience
Volume 2017, Article ID 3162571, 11 pages
https://doi.org/10.1155/2017/3162571
Research Article

Forest Pruning Based on Branch Importance

1School of Automation, Xi’an University of Posts and Telecommunication, Xi’an, Shaanxi 710121, China
2School of Computer and Information Technology, Xinyang Normal University, Xinyang, Henan 464000, China

Correspondence should be addressed to Huaping Guo; moc.361@mc_ougph

Received 19 January 2017; Revised 29 March 2017; Accepted 30 April 2017; Published 1 June 2017

Academic Editor: Michael Schmuker

Copyright © 2017 Xiangkui Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.