论文标题
针对具有相似功能的数据集的功能选择稳定性的调整措施
Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features
论文作者
论文摘要
对于具有相似功能的数据集，例如高度相关的功能，大多数现有的稳定性措施的行为是不希望的：它们认为几乎相同但具有不同标识符的功能是不同的功能。现有的调整后稳定性度量，即考虑到特征之间相似性的稳定度量，具有主要的理论缺点。我们引入了克服这些缺点的新调整稳定性措施。我们将它们相互比较，并根据人工和实际特征集的现有稳定性度量进行比较。根据结果，我们建议使用一种新的稳定度量，该方法将高度相似的特征视为可交换的功能。
For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost identical but have different identifiers as different features. Existing adjusted stability measures, that is, stability measures that take into account the similarities between features, have major theoretical drawbacks. We introduce new adjusted stability measures that overcome these drawbacks. We compare them to each other and to existing stability measures based on both artificial and real sets of selected features. Based on the results, we suggest using one new stability measure that considers highly similar features as exchangeable.
