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About Feature Selection For MLP Neural Network
This paper presents a new wrapper-based feature selection method for multilayer perceptron (MLP) neural networks. It uses a feature ranking criterion to measure the importance of a feature by computing the aggregate difference, over the feature space, of the probabilistic outputs of the MLP with and without the feature. Thus, a score of importance with respect to every feature can be provided using this criterion. Based on the numerical experiments on several artificial and real-world data sets,
Detailed Information
| Author: | ['a'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 23 |
| Language: | English |
| Format: | |
| Price: | FREE |
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