• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Pattern Recognition in Bioinformatics >A Study on the Importance of Differential Prioritization in Feature Selection Using Toy Datasets
【24h】

A Study on the Importance of Differential Prioritization in Feature Selection Using Toy Datasets

机译：使用玩具数据集的特征选择中区分优先级重要性的研究

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

Previous empirical works have shown the effectiveness of differential prioritization in feature selection prior to molecular classification. We now propose to determine the theoretical basis for the concept of differential prioritization through mathematical analyses of the characteristics of predictor sets found using different values of the DDP (degree of differential prioritization) from realistic toy datasets. Mathematical analyses based on analytical measures such as distance between classes are implemented on these predictor sets. We demonstrate that the optimal value of the DDP is capable of forming a predictor set which consists of classes of features which are well separated and are highly correlated to the target classes - a characteristic of a truly optimal predictor set. From these analyses, the necessity of adjusting the DDP based on the dataset of interest is confirmed in a mathematical manner, indicating that the DDP-based feature selection technique is superior to both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods. Applying similar analyses to real-life multiclass microarray datasets, we obtain further proof of the theoretical significance of the DDP for practical applications.
机译：先前的经验工作表明，在分子分类之前，在特征选择中采用不同优先级排序是有效的。我们现在建议通过对使用实际玩具数据集的DDP的不同值（差分优先级）找到的预测变量特征进行数学分析，确定差分优先级概念的理论基础。在这些预测变量集上执行基于分析度量（例如类之间的距离）的数学分析。我们证明了DDP的最优值能够形成一个预测器集，该预测器集由特征类别组成，这些特征类别之间具有很好的分离性，并且与目标类别高度相关-这是真正最佳的预测器集的特征。从这些分析中，以数学方式确定了基于感兴趣的数据集调整DDP的必要性，这表明基于DDP的特征选择技术优于基于简单等级的选择和最先进的等值技术。 -优先评分方法。将类似的分析应用于现实生活中的多类微阵列数据集，我们获得了DDP在实际应用中的理论意义的进一步证明。

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