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Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints
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DOI：10.1109/TCYB.2021.3087776.png)
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摘要
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Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that introduces two constraints, the satisfaction of which leads to a sparse FS layer. We performed extensive experiments on synthetic and real-world data to evaluate the performance of our proposed FS method. In the experiments, we focus on high-dimensional, low-sample-size data since they represent the main challenge for FS. The results confirm that the proposed FS method based on a sparse neural-network layer with normalizing constraints (SNeL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.
Keyword:
Neurons
Input variables
Training
Standards
Feedforward neural networks
Feature extraction
Deep learning
Dimensionality reduction
feature selection (FS)
high-dimensional data
neural network
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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