摘要
在集成学习领域,传统的动态集成选择需要为每一个样本选择子分类器组成集成分类器,这极大地增加了计算复杂度。针对这一问题,提出一种新的半动态集成选择方法。该方法分为两阶段,第一阶段为所有的测试样本选择最好的个体分类器组成一个集成分类器,第二阶段从剩余的个体分类器集合中为当前测试样本动态地选择子分类器组成一个集成分类器。最终的分类结果通过融合两阶段得到集成分类器的结果得到。通过对UCI数据测试的结果表明,该算法不仅能取得较好的分类性能,而且能极大地降低计算复杂度。
Traditional Dynamic Ensemble Selection （ DES） in ensemble learning needs to select individual classifiers for all the test samples.However, it leads to highly computational cost.Due to this issue, a new Semi Dynamic Ensemble Selection （ Semi-DES） strategy is proposed in this paper, which consists of two stages.Individual classifiers are selected for all the test samples in the first stage.In the second stage, the classifiers for each test sample are selected dynamically.The final result is obtained by integrating the output of the two stages.The experimental results on UCI data set demonstrate the proposed method can obtain a better classification performance.Moreover, Semi-DES can reduce the computational cost greatly.
出处
《计算机与现代化》
2015年第2期48-51,共4页
Computer and Modernization
关键词
集成学习
选择性集成
动态集成选择
分类
ensemble learning
selective ensemble
dynamic ensemble selection
classification
