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Automatic Determination of Size for Feature Selection

机译:自动确定特征选择的大小

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In pattern recognition, feature selection is an important technique to improve the performance of classifiers designed from a limited number of training samples. So far, many algorithms have been proposed, but one of the problems is that it is hard to determine the size of a feature subset to be selected. In this paper, we discuss this issue and propose a novel method to determine the size automatically. This method is applicable to large-scale problems with features over fifty. Through some experiments with synthetic data the approach is shown to work almost optimally.
机译:在模式识别中,特征选择是提高从有限数量的训练样本设计的分类器性能的重要技术。到目前为止,已经提出了许多算法,但其中一个问题是难以确定要选择的特征子集的大小。在本文中,我们讨论了这个问题并提出了一种新的方法来自动确定尺寸。该方法适用于超过五十的特征的大规模问题。通过一些具有合成数据的实验,该方法将显示几乎最佳地工作。

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