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Supervised feature selection using sparse evolutionary training and neuron strength
Girdziunas, K.G. (2021) Supervised feature selection using sparse evolutionary training and neuron strength.
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| Abstract: | Feature selection has been used to battle the ever-increasing dimensionality of datasets usedfor machine learning applications. Many feature selection methods, such as the Chi-Square test andLaplacian score, determine feature importance via a hand-crafted metric, often tailored to a specifictype of dataset. This paper proposes a method for deciding feature importance by training a supervisedsparse neural network model usingSparse Evolutionary Trainingand scoring features depending onNeuron Strength. The features are selected in one shot after a network has been trained and, canoutperform Chi-Square test feature selection, performing best in image recognition tasks. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Electrical Engineering BSc (56953) |
| Link to this item: | https://purl.utwente.nl/essays/87646 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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