• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >A distance-based weighting framework for boosting the performance of dynamic ensemble selection
【24h】

A distance-based weighting framework for boosting the performance of dynamic ensemble selection

机译：促进动态集合选择性能的距离为基础的权重框架

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework.
机译：动态集合选择（DES）策略是处理分类问题的机器学习中最常见有效的技术之一。 DES系统旨在构造由根据各个分类器的能力级别的从候选分类器池中选择的最合适的分类器组成的集合。由于选择了几分类器，因此它们的组合变得至关重要。然而，大多数当前的DES接近所选分类器的组合，同时忽略所需查询样本的本地信息。为了提高DES的分类系统的性能，我们本文提出了在获得DES系统的最终输出期间对分类器融合的动态加权框架。特别地，所提出的方法首先使用DES方法来获得查询样本的一组分类器。然后，基于对共识的分析获得所选集合的假设载体。最后，开发了一种基于距离的加权方案以根据查询样本的近距离调整假设矢量。所提出的方法在30个现实世界数据集上进行测试，其具有基于均匀和异构集​​合的六种众所周知的DES方法。通过适当的统计测试支持的结果，表明我们的方法优于准确性和Kappa措施，原始DES框架。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号