基于迹比准则的并行特征选择
Parallel feature selection based on the trace ratio criterion
摘要：
当今数据的增长对管理和推理提出了挑战。虽然特征提取方法能够减少用于推断的数据的大小，但是它们无助于最小化数据存储的成本。另一方面，特征选择有助于去除冗余特征，因此不仅有助于推理，而且有助于降低管理成本。本文提出了一种新的并行特征选择分类方法--基于迹准则的并行特征选择方法（PFST），该方法可以扩展到非常大的数据集。我们的方法使用跟踪标准，一种用于Fisher判别分析的类可分性度量，来评估特征的有用性。从理论上分析了该准则的理想性质。基于该准则，PFST通过首先进行前向选择并提前去除看似冗余的特征，快速地从大数据集的一组特征中找到重要特征。在模型中包含最重要的特征之后，我们检查它们的贡献，以确定可能改善拟合的可能相互作用。最后，我们做了一个向后选择，以检查回可能的冗余添加的前向步骤。我们评估我们的方法，通过各种实验使用线性判别分析作为分类器上选定的功能。实验表明，我们的方法可以产生一个小的功能集在一小部分的时间量的其他方法进行比较。此外，在PFST选择的特征上训练的分类器不仅比其他方法选择的分类器具有更好的准确性，而且比所有可用特征的分类器具有更好的准确性。
英文摘要：
The growth of data today poses a challenge in management and inference. While feature extraction methods are capable of reducing the size of the data for inference, they do not help in minimizing the cost of data storage. On the other hand, feature selection helps to remove the redundant features and therefore is helpful not only in inference but also in reducing management costs. This work presents a novel parallel feature selection approach for classification, namely Parallel Feature Selection using Trace criterion (PFST), which scales up to very large datasets. Our method uses trace criterion, a measure of class separability used in Fisher's Discriminant Analysis, to evaluate feature usefulness. We analyzed the criterion's desirable properties theoretically. Based on the criterion, PFST rapidly finds important features out of a set of features for big datasets by first making a forward selection with early removal of seemingly redundant features parallelly. After the most important features are included in the model, we check back their contribution for possible interaction that may improve the fit. Lastly, we make a backward selection to check back possible redundant added by the forward steps. We evaluate our methods via various experiments using Linear Discriminant Analysis as the classifier on selected features. The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison. In addition, the classifier trained on the features selected by PFST not only achieves better accuracy than the ones chosen by other approaches but can also achieve better accuracy than the classification on all available features.
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