Συγκριτική μελέτη γραφοθεωρητικών αλγορίθμων ημι-επιτηρούμενης μηχανικής μάθησης σε προβλήματα ταξινόμησης με μεγάλη ταξική ανισορροπία

Αλεξανδροπούλου, Χαρίκλεια K.

Comparative study of graph-based semi-supervised machine learning algorithms on classification problems with extreme class imbalance

Master Thesis

Author

Αλεξανδροπούλου, Χαρίκλεια K.

Date

2013-05

Abstract

This thesis, entitled “Comparative Study of Graph-Based Semi-Supervised Machine Learning Algorithms on Classification Problems with Extreme Class Imbalance”, is a description, analysis and implementation of graph-based Semi-Supervised Learning algorithms. Initially we attempt to bibliographic approach the field of machine learning with partial supervision. Then, the algorithms are analyzed theoretically and applied to data classification of classes with large class imbalance. An applied correction method is also used. This method uses the prior knowledge of the classes. Finally, we propose a new correction method, based on the correct information which is obtained from the previous one and this information is being further developed.

Postgraduate Studies Programme

Προηγμένα Συστήματα Πληροφορικής

Department

Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Πληροφορικής

Number of pages

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/8283

Collections

Τμήμα Πληροφορικής

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