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- W4386634211 abstract "In feature selection research, simultaneous multiclass 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." @default.
- W4386634211 created "2023-09-13" @default.
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- W4386634211 date "2023-01-01" @default.
- W4386634211 modified "2023-10-18" @default.
- W4386634211 title "A unified multi-class feature selection framework for microarray data" @default.
- W4386634211 doi "https://doi.org/10.1109/tcbb.2023.3314432" @default.
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- W4386634211 hasPublicationYear "2023" @default.
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