首页> 外文会议>International conference on communications and cyber physical engineering >Significant Improvement in Classification Performance Metrics by Ensemble Approach
【24h】

Significant Improvement in Classification Performance Metrics by Ensemble Approach

机译:通过集合方法显着改善分类性能指标

获取原文

摘要

Today most of real time applications are source of big data and classification process over huge amount of this data is a challenge. The training of a single classifier with such type of large amount of data causes plasticity-stability problem. A single classifier is not able to preserve large amount of knowledge when it starts to learn new knowledge. This paper gives the introduction about the ensemble and techniques to generate ensemble which shows that how one can maintain stability between bias and variance to improve classification performance. Various classification performance metrics are elaborated and effect of ensemble size on different evaluation measures is also demonstrated.
机译:今天,大多数实时应用都是大数据的来源和大量这些数据的分类过程是一项挑战。具有这种类型的大量数据的单个分类器的训练导致可塑性稳定性问题。单个分类器无法在开始学习新知识时保留大量知识。本文介绍了生成集合的集合和技术的介绍,这表明如何在偏差和方差之间保持稳定性以提高分类性能。还阐述了各种分类性能指标,并阐述了对不同评估措施的集合尺寸的影响。

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