.png){kind=icon}
.png){kind=icon}
Return
A Unified Multi-Class Feature Selection Framework for Microarray Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2023.3314432.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Feature extraction
Training
Support vector machines
Task analysis
Optimization
Standards
Radial basis function networks
Multi-class feature selection
randomization
feature ranking criterion
microarray data
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K