Feature selection for nonlinear models with extreme learning machines
Feature selection for nonlinear models with extreme learning machines
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DOI：
10.1016/j.neucom.2011.12.055
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发表时间：
2013-02-15
期刊：
影响因子：
6
通讯作者：
Lendasse, Amaury
中科院分区：
文献类型：
--
作者：
Benoit, Frenay;van Heeswijk, Mark;Lendasse, Amaury
在特征选择的背景下，在选择的特征数量和泛化误差之间存在权衡。两个图可以帮助总结特征选择：特征选择路径和稀疏误差权衡曲线。特征选择路径显示每个子集大小的最佳特征子集，而稀疏性-误差权衡曲线显示相应的泛化误差。这些图形化工具可以帮助专家选择合适的特征子集，提取有用的领域知识。为了获得这些工具，这里使用了极端学习机，因为它们训练速度快，并且可以很容易地使用PRESS统计数据获得其泛化误差的估计。介绍了一种算法，该算法在标准的极限学习机器上增加了一个额外的层，以优化所选特征的子集。实验结果表明了该方法的有效性。(C) 2012 Elsevier B.V.版权所有
In the context of feature selection, there is a trade-off between the number of selected features and the generalisation error. Two plots may help to summarise feature selection: the feature selection path and the sparsity-error trade-off curve. The feature selection path shows the best feature subset for each subset size, whereas the sparsity-error trade-off curve shows the corresponding generalisation errors. These graphical tools may help experts to choose suitable feature subsets and extract useful domain knowledge. In order to obtain these tools, extreme learning machines are used here, since they are fast to train and an estimate of their generalisation error can easily be obtained using the PRESS statistics. An algorithm is introduced, which adds an additional layer to standard extreme learning machines in order to optimise the subset of selected features. Experimental results illustrate the quality of the presented method. (C) 2012 Elsevier B.V. All rights reserved.