基于随机化属性选择和邻域覆盖约简的集成学习

摘要

Improving accuracy, robustness and understandability is the objective of classification modeling. Regarding instability and performance limitation of existing rule learning techniques, we introduce an ensemble classifier based on randomized neighborhood reduction and neighborhood covering reduction. A set of reducts are obtained with randomized attribute reduction. A collection of rule sets are derived from the reducts based on neighborhood covering reduction. And then the classification result is output by combining the classification decision of different rule sets. The experiment result shows that the proposed technique is better than or equal to other classifiers, and is more stable when deals with noisy data.%提高分类模型的分类精度和可靠性是分类建模追求的目标.针对目前规则学习方法应用于分类时稳定性差以及分类精度低的问题,本文通过随机化邻域属性约简,搜索一组分类精度较高的属性子集,在不同的属性子集上采用邻域覆盖约简方法学习分类规则,得到多个规则集.最后通过简单投票融合不同规则集上的分类结果获得对象的类别.实验表明,基于随机化邻域约简的集成学习方法分类性能优于或与其它相关的分类器相当,并且在噪声扰动下具有更强的鲁棒性.