邻域决策的随机约简与集成分类研究

Neighborhood Based Randomized Reduction and Ensemble Classification Approach

为了进一步提升邻域分类器的性能,提出基于属性约简的集成邻域分类策略.首先在启发式求解约简的过程中,通过放宽属性选择的条件,从而在一定范围内利用随机选择的方法获取多个能够降低邻域决策错误率的属性子集,然后借助这些属性子集在对应邻域分类器上得到的结果进行投票集成,得到最终的分类类别.在12个UCI数据集上的实验结果表明,所提出的基于属性约简的集成邻域分类策略不仅能够有效地提升邻域分类器的分类精度,而且亦能增强邻域分类结果的鲁棒性.这一研究为从集成的视角研究粗糙集理论提供了技术支持.

To further improve the performance of neighborhood classifier,an ensemble strategy for neighborhood classifier is proposed based on randomized reduction. Firstly,a random parameter is introduced into the heuristic process for computing multi-different reducts that reduce the decision error of neighborhood classifier. Secondly,the voting ensemble is employed for fusing the neighborhood classification results by these reducts. The experimental results on 12 UCI data sets tell us the proposed strategy can improve not only the classification accuracy but also the classification robustness. This study provides us a technique for studying rough set theory via ensemble learning strategy.