摘要
为了提高分类器集成性能,提出了一种基于聚类算法与排序修剪结合的分类器集成方法。将混淆矩阵作为量化基分类器间差异度的工具,通过聚类将分类器划分为若干子集,提出一种排序修剪算法,以距离聚类中心最近的分类器为起点,根据分类器的距离对差异度矩阵动态加权,以加权差异度作为排序标准对子集中的分类器进行按比例修剪,最后使用投票法对选出的基分类器进行集成。与多种集成方法在UCI数据库中的10组数据集上进行对比与分析,实验结果表明,基于聚类与排序修剪的分类器选择方法有效提升了集成系统的分类能力。
In order to enhance the performance of classifier ensemble,this paper proposed a selective ensemble method based on clustering and sorting pruning. Firstly,it used confusion matrix as a tool to quantify the difference between the base classifiers,and divided the classifiers into several subsets by clustering. Then it proposed the pruning algorithm,took the nearest cluster center classifier as a starting point,which gave the weights of the diversity matrix dynamically according to the distance of classifier. It scaled the subsets by weighted diversity which was the sorting standard. Finally,it used voting method to integrate the selective classifiers. Moreover this paper performed comparative experiments and analysis on 10 datasets from UCI database with other ensemble methods. The experimental results show that this approach can effectively improve the classification ability of the ensemble system.
引文
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