• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Brazilian Conference on Intelligent Systems >Building Ensembles with Classifier Selection Using Self-Organizing Maps
【24h】

Building Ensembles with Classifier Selection Using Self-Organizing Maps

机译：使用自组织映射与分类器选择进行建筑集成

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Improving the performance of supervised classification methods is a subject of many literature works. An efficient strategy is the adoption of an ensemble of classifiers to divide the classification problem. In ensembles with classifier selection, there is no fusion of the classifiers decisions. A particular classifier is selected according to the input data instead. In this paper, well-known clustering methods based on self-organizing structures are used to implement ensembles with classifier selection. The self-organizing structures are used to detect the topological structure of data and help to divide the problem into smaller and easier sub-problems to solve. Experiments with different datasets show that the use of clustering methods to perform the classifier selection can contribute to split the problem and improve the classification accuracy compared to some traditional strategies. Additionally, the results encourage the development of more research to find out other ways to split problems using data clustering techniques.
机译：改进监督分类方法的性能是许多文献工作的主题。一种有效的策略是采用分类器集合来划分分类问题。与分类器选择合奏时，分类器决策不会融合在一起。而是根据输入数据选择特定的分类器。在本文中，使用了基于自组织结构的著名聚类方法来实现带有分类器选择的集合。自组织结构用于检测数据的拓扑结构，并有助于将问题分为更小的和更容易解决的子问题。在不同数据集上进行的实验表明，与某些传统策略相比，使用聚类方法执行分类器选择可以有助于分解问题并提高分类精度。此外，结果鼓励进行更多的研究，以发现使用数据聚类技术拆分问题的其他方法。

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