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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:http://purl.utwente.nl/essays/87646
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