IEEE Trans Cybern. 2015 Feb;45(2):177-90. doi: 10.1109/TCYB.2014.2322195. Epub 2014 May 20.
Traditional random subspace-based classifier ensemble approaches (RSCE) have several limitations, such as viewing the same importance for the base classifiers trained in different subspaces, not considering how to find the optimal random subspace set. In this paper, we design a general hybrid adaptive ensemble learning framework (HAEL), and apply it to address the limitations of RSCE. As compared with RSCE, HAEL consists of two adaptive processes, i.e., base classifier competition and classifier ensemble interaction, so as to adjust the weights of the base classifiers in each ensemble and to explore the optimal random subspace set simultaneously. The experiments on the real-world datasets from the KEEL dataset repository for the classification task and the cancer gene expression profiles show that: 1) HAEL works well on both the real-world KEEL datasets and the cancer gene expression profiles and 2) it outperforms most of the state-of-the-art classifier ensemble approaches on 28 out of 36 KEEL datasets and 6 out of 6 cancer datasets.
传统的基于随机子空间的分类器集成方法（RSCE）存在一些局限性，例如对在不同子空间中训练的基础分类器同等重视，不考虑如何找到最优的随机子空间集。在本文中，我们设计了一种通用的混合自适应集成学习框架（HAEL），并将其应用于解决 RSCE 的局限性。与 RSCE 相比，HAEL 由两个自适应过程组成，即基础分类器竞争和分类器集成交互，以便调整每个集成中的基础分类器的权重，并同时探索最优的随机子空间集。在分类任务的 KEEL 数据集库中的真实数据集和癌症基因表达谱上的实验表明：1）HAEL 在真实的 KEEL 数据集和癌症基因表达谱上表现良好；2）它在 36 个 KEEL 数据集中的 28 个和 6 个癌症数据集中的 6 个数据集上优于大多数最先进的分类器集成方法。
