删除最差基学习器来层次修剪Bagging集成

主要目的是寻找到一种Bagging的快速修剪方法,以缩小算法占用的存储空间、提高运算的速度和实现提高分类精度的潜力.传统的选择性集成方法研究的重点是基学习器之间的差异化,从同质化的角度采研究这一问题,提出了一种全新的选择性集成思路.通过选择基学习器集合中的最差者来对Bagging集成进行快速层次修剪,获得了一种学习速度接近Bagging性能在其基础上得到提高的新算法.新算法的训练时间明显小于GASEN而性能与其相近.该算法同时还保留了与Bagging相同的并行处理能力.

Hierachical Bagging Ensemble Pruning Based on Deleting Worst Base Learner

The problem of selecting the best combination of classifiers from an ensemble has been shown to be NP-complete. Several strategies have been used to reduce the number of the units in ensemble classifier. The main objective of selective ensemble is to find a rapid pruning method for bagging to reduce the storage needs,speed up the classification process and obtain the potential of improving the classification accuracy. Those traditional methods of selective ensemble focus on the diversity of base learners. D...