• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Australasian joint conference on artificial intelligence >An Effective Method for Imbalanced Time Series Classification: Hybrid Sampling
【24h】

An Effective Method for Imbalanced Time Series Classification: Hybrid Sampling

机译：时间序列不平衡分类的一种有效方法：混合采样

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

摘要

Most traditional supervised classification learning algorithms are ineffective for highly imbalanced time series classification, which has received considerably less attention than imbalanced data problems in data mining and machine learning research. Bagging is one of the most effective ensemble learning methods, yet it has drawbacks on highly imbalanced data. Sampling methods are considered to be effective to tackle highly imbalanced data problem, but both over-sampling and under-sampling have disadvantages; thus it is unclear which sampling schema will improve the performance of bagging predictor for solving highly imbalanced time series classification problems. This paper has addressed the limitations of existing techniques of the over-sampling and under-sampling, and proposes a new approach, hybrid sampling technique to enhance bagging, for solving these challenging problems. Comparing this new approach with previous approaches, over-sampling, SPO and under-sampling with various learning algorithms on benchmark data-sets, the experimental results demonstrate that this proposed new approach is able to dramatically improve on the performance of previous approaches. Statistical tests, Friedman test and Post-hoc Nemenyi test are used to draw valid conclusions.
机译：大多数传统的监督分类学习算法对于高度不平衡的时间序列分类都是无效的，与数据挖掘和机器学习研究中的不平衡数据问题相比，这种方法受到的关注要少得多。套袋是最有效的整体学习方法之一，但是它在高度不平衡的数据上有缺点。采样方法被认为是解决高度不平衡的数据问题的有效方法，但是过采样和欠采样都有缺点。因此，目前尚不清楚哪种采样方案将提高装袋预测器的性能，以解决高度不平衡的时间序列分类问题。本文解决了过采样和欠采样的现有技术的局限性，并提出了一种新方法，即混合采样技术来增强装袋，以解决这些难题。将这种新方法与以前的方法（过采样，SPO和欠采样）与基准数据集上的各种学习算法进行比较，实验结果表明，该提议的新方法能够显着改善以前方法的性能。统计检验，Friedman检验和事后Nemenyi检验可得出有效的结论。

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