• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Tree-Structured Feature Extraction Using Mutual Information
【24h】

Tree-Structured Feature Extraction Using Mutual Information

机译：互信息的树状特征提取

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

One of the most informative measures for feature extraction (FE) is mutual information (MI). In terms of MI, the optimal FE creates new features that jointly have the largest dependency on the target class. However, obtaining an accurate estimate of a high-dimensional MI as well as optimizing with respect to it is not always easy, especially when only small training sets are available. In this paper, we propose an efficient tree-based method for FE in which at each step a new feature is created by selecting and linearly combining two features such that the MI between the new feature and the class is maximized. Both the selection of the features to be combined and the estimation of the coefficients of the linear transform rely on estimating 2-D MIs. The estimation of the latter is computationally very efficient and robust. The effectiveness of our method is evaluated on several real-world data sets. The results show that the classification accuracy obtained by the proposed method is higher than that achieved by other FE methods.
机译：互信息（MI）是用于特征提取（FE）的最具信息量的措施之一。就MI而言，最佳FE会创建新功能，这些新功能对目标类别的依赖最大。但是，获得高维MI的准确估算以及对其进行优化并不总是那么容易，尤其是在只有少量训练集可用的情况下。在本文中，我们提出了一种有效的基于树的有限元方法，其中在每个步骤中都通过选择和线性组合两个特征来创建新特征，从而使新特征和类之间的MI最大化。要组合的特征的选择和线性变换的系数的估计都依赖于估计2-D MI。后者的估计在计算上非常有效且可靠。我们的方法的有效性在几个实际数据集上进行了评估。结果表明，该方法获得的分类精度高于其他有限元方法。

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