论文标题
梁搜索功能选择
Beam Search for Feature Selection
论文作者
论文摘要
在本文中，我们介绍并证明了使用特征子集的分类模型性能的一致性结果。此外，我们建议使用光束搜索执行特征选择，可以将其视为正向选择的概括。我们通过使用不同的特征集评估和比较不同分类模型的性能，将光束搜索应用于模拟和现实世界数据。结果表明，光束搜索可以胜过向前的选择，尤其是当特征相关时，因此当共同考虑到共同的歧视功率时，比单独的功能更具歧视能力。此外，在某些情况下，分类模型只能使用Beam搜索选择的十个功能而不是数百个原始功能来获得可比的性能。
In this paper, we present and prove some consistency results about the performance of classification models using a subset of features. In addition, we propose to use beam search to perform feature selection, which can be viewed as a generalization of forward selection. We apply beam search to both simulated and real-world data, by evaluating and comparing the performance of different classification models using different sets of features. The results demonstrate that beam search could outperform forward selection, especially when the features are correlated so that they have more discriminative power when considered jointly than individually. Moreover, in some cases classification models could obtain comparable performance using only ten features selected by beam search instead of hundreds of original features.
