数据集特征对选择稳定性的影响

Salem Alelyani, Huan Liu, Lei Wang
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引用次数: 36

摘要

特征选择是一种有效的降低数据集的维数并为领域问题选择相关特征的技术。近年来,特征选择方法的稳定性越来越受到人们的关注。事实上,除了学习性能之外,它已经成为决定特征选择算法好坏的关键因素。在这项工作中,我们使用数据集的真实性和不同的知名特征选择算法进行了广泛的实验研究,以研究这些算法在稳定性方面的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effect of the Characteristics of the Dataset on the Selection Stability
Feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.
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