IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3725-3736. doi: 10.1109/TCBB.2023.3314432. Epub 2023 Dec 25.
In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary feature selection algorithms. However, extending existing RFE algorithms to multi-class tasks may increase the computational cost and lead to performance degradation. With this motivation, we introduce a unified multi-class feature selection (UFS) framework for randomization-based neural networks to address these challenges. First, we propose a new multi-class feature ranking criterion using the output weights of neural networks. The heuristic underlying this criterion is that "the importance of a feature should be related to the magnitude of the output weights of a neural network". Subsequently, the UFS framework utilizes the original features to construct a training model based on a randomization-based neural network, ranks these features by the criterion of the norm of the output weights, and recursively removes a feature with the lowest ranking score. Extensive experiments on 15 real-world datasets suggest that our proposed framework outperforms state-of-the-art algorithms. The code of UFS is available at https://github.com/SVMrelated/UFS.git.
在特征选择研究中，同时进行多类特征选择技术很受欢迎，因为它们可以同时为所有类别选择有信息的特征。递归特征消除（RFE）方法是一种先进的二进制特征选择算法。然而，将现有的 RFE 算法扩展到多类任务可能会增加计算成本并导致性能下降。基于这一动机，我们引入了一个基于随机化神经网络的统一多类特征选择（UFS）框架来解决这些挑战。首先，我们提出了一种新的多类特征排序准则，该准则使用神经网络的输出权重。这个准则的基本原理是“一个特征的重要性应该与神经网络的输出权重的大小有关”。随后，UFS 框架利用原始特征基于随机化神经网络构建一个训练模型，根据输出权重的范数准则对这些特征进行排序，并递归地删除排名最低的特征。在 15 个真实数据集上的广泛实验表明，我们提出的框架优于最先进的算法。UFS 的代码可在 https://github.com/SVMrelated/UFS.git 上获得。
