dc.contributor.author | Μπαρμπάκου, Δωροθέα | el |
dc.contributor.author | Barmpakou, Dorothea | en |
dc.date.accessioned | 2019-12-20T10:03:12Z | |
dc.date.available | 2019-12-20T10:03:12Z | |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/49604 | |
dc.identifier.uri | http://dx.doi.org/10.26240/heal.ntua.17302 | |
dc.description | Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Εφαρμοσμένες Μαθηματικές Επιστήμες” | el |
dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ | * |
dc.subject | Classification | en |
dc.subject | Κατηγοριοποίηση | el |
dc.subject | Κλασικές Εφαρμογές | el |
dc.subject | Dimensionality Reduction | en |
dc.subject | Classical Applications | en |
dc.subject | Modern Approaches | en |
dc.subject | Methods | en |
dc.subject | Mείωση Διαστάσεων | el |
dc.subject | Σύγχρονες Μέθοδοι | el |
dc.subject | Μέθοδοι | el |
dc.title | Classical and Modern Approaches to Classification and Dimensionality Reduction Techniques | en |
dc.title | Κλασικές και Σύγχρονες Μέθοδοι για Κατηγοριοποίηση και Μείωση Διαστάσεων | el |
heal.type | masterThesis | |
heal.classification | Statistics | en |
heal.classification | Στατιστική | el |
heal.language | en | |
heal.access | free | |
heal.recordProvider | ntua | el |
heal.publicationDate | 2019-06-27 | |
heal.abstract | In this thesis, we focus on techniques for dimensionality reduction and classi cation problems, which facilitate the statistical analysis and interpretation of complex data. In Chapter 1, we present Principal Components Analysis (PCA): a dimensionality reduction technique. We introduce its aim and the theoretical basis, we de ne the properties of Principal Components and their correlation structure. The loadings, component scores and correlation circle are analysed. Methods for extracting the appropriate number of Principal Components are included. Furthermore, we carry out a classical and a modern application of PCA to two di erent datasets. Speci cally, we describe and inspect the Irish dataset, in which the number of variables is lower than the number of the individuals (classical application), and the Chicken dataset which includes far fewer individuals than variables (modern application). In Chapter 2, Classi cation is introduced and some of the most important parametric classi ers are analysed. Firstly, we introduce Logistic Regression Analysis, the interpretation and estimation of its coe cients and the ROC Curve and we apply it to the Irish dataset. Then, Linear Discriminant Analysis is introduced, its method and application to the Irish data. Lastly, the theoretical basis of Quadratic Discriminant Analysis is presented and its application to the Irish dataset as well. In Chapter 3, we introduce K Nearest Neighbors non parametric method for classi cation, its method and application to the Irish dataset and to a more complex one: Khan dataset. We extract important insights. Chapter 4 is devoted to methods based on Trees. More precisely, Classi cation Trees and Regression Trees methods are analysed. Regarding the Classi cation Trees, we introduce the method, present the building procedure of a classi cation tree, the tree pruning and some advantages of Classi cation Trees method, and we apply it to the Irish dataset. Regarding the Regression Trees, we introduce the method and the pruning procedure, and we apply it to the Boston dataset. Finally, Chapter 5 includes important remarks and conclusions, taking into account all of the methods applied to the Irish data. | en |
heal.advisorName | Barranco--Chamorro, Inmaculada | en |
heal.advisorName | Καρώνη, Χρυσηΐς | el |
heal.committeeMemberName | Καρώνη, Χρυσηΐς | el |
heal.committeeMemberName | Barranco--Chamorro, Inmaculada | es |
heal.committeeMemberName | Fernández Ponce, Fernández Ponce | el |
heal.academicPublisher | Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Εφαρμοσμένων Μαθηματικών και Φυσικών Επιστημών | el |
heal.academicPublisherID | ntua | |
heal.numberOfPages | 118 σ. | el |
heal.fullTextAvailability | true |
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