Feature Selection Using Neural Network Pruning

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dc.contributor.advisor Pelillo, Marcello it_IT
dc.contributor.author Scalco, Alberto <1993> it_IT
dc.date.accessioned 2019-02-18 it_IT
dc.date.accessioned 2019-06-11T08:42:01Z
dc.date.available 2019-06-11T08:42:01Z
dc.date.issued 2019-03-07 it_IT
dc.identifier.uri http://hdl.handle.net/10579/14382
dc.description.abstract Feature selection is a well known technique for data prepossessing with the purpose of removing redundant and irrelevant information with the benefits, among others, of an improved generalization and a decreased curse of dimensionality. This paper investigates an approach based on a trained neural network model, where features are selected by iteratively removing a node in the input layer. This pruning process, comprise a node selection criterion and a subsequent weight correction: after a node elimination, the remaining weights are adjusted in a way that the overall network behaviour do not worsen over the entire training set. The pruning problem is formulated as a system of linear equations solved in a least-squares sense. This method allows the direct evaluation of the performance at each iteration and a stopping condition is also proposed. Finally experimental results are presented in comparison to another feature selection method. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Alberto Scalco, 2019 it_IT
dc.title Feature Selection Using Neural Network Pruning it_IT
dc.title.alternative Feature Selection Using Neural Network Pruning it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2017/2018, sessione straordinaria it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 846175 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend it_IT
dc.provenance.upload Alberto Scalco (846175@stud.unive.it), 2019-02-18 it_IT
dc.provenance.plagiarycheck Marcello Pelillo (pelillo@unive.it), 2019-03-04 it_IT


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