Maximizing diversity by transformed ensemble learning
Maximizing diversity by transformed ensemble learning
复制标题
通过转变集成学习来最大化多样性
DOI：
10.1016/j.asoc.2019.105580
复制
发表时间：
2019-09-01
影响因子：
8.7
通讯作者：
Wang, Rongfang
中科院分区：
文献类型：
--
作者：
Mao, Shasha;Chen, Jia-Wei;Wang, Rongfang
The diversity and the individual accuracies in an ensemble system are usually two opposite objects, which is ignored in most preliminary ensemble learning algorithms. To alleviate this issue, in this paper, a novel weighted ensemble learning is proposed by maximizing the diversity and the individual accuracy simultaneously. More specifically, in the proposed framework, the combination of multiple base learners is converted into a linear transformation of all these base learners, and the optimal weights are obtained by pursuing the optimal projective direction of the linear transformation. Then the derived objective function can be efficiently solved by the alternating directional multiplier method. Finally, the proposed method is verified on UCI datasets and face databases, and the experimental results illustrate that the proposed method effectively improves the performance compared with other ensemble methods. (C) 2019 Elsevier B.V. All rights reserved.
