Visualizing Breast Cancer Data with t-SNE

dc.contributor.advisorSprinkhuizen-Kuyper, I.G.
dc.contributor.advisorMarchiori, E.
dc.contributor.authorTam, M.W.
dc.date.issued2013-08-28
dc.description.abstractOne in eight women will get breast cancer in her lifetime and in 2008 it has caused 458.503 deaths among the world [15]. Despite that technology has made considerable improvements in the last decades, there is still room for more advances. A technique that possibly can contribute to this field is t-SNE [24]. The aim of this thesis is to investigate whether t-SNE is able to present the breast cancer data in an interpretable way and possibly improves the classification performances. We employ two approaches to explore the applicability of t-SNE. In the first approach we compare the visualizations and in the second approach the classification performances are compared. We found that classification on the original data per- formed significantly better than on t-SNE data. This suggests that t-SNE is not applicable to the breast cancer data set.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/134
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleVisualizing Breast Cancer Data with t-SNEen_US
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