Deep-Learning vs Classical Machine-Learning comparison for text classification
Authors: Δρίζης, Ιωάννης 
Advisor: Triantafyllou, Ioannis 
Committee Member: Kouis, Dimitrios 
Publisher: University of West Attica (UNIWA)
Issue Date: 8-Jul-2022
Program: Μεταπτυχιακές διπλωματικές εργασίες ΠΜΣ Διαχείριση Πληροφοριών σε Βιβλιοθήκες, Αρχεία, Μουσεία
Keywords: Machine learning, Deep learning, Text preprocessing, Text classification, Devmax.df, Μηχανική μάθηση, Βαθιά μηχανική μάθηση, Προεπεξεργασία κειμένων, Κατηγοριοποίηση κειμένων
Abstract: 
The Classical Machine Learning and Deep Learning models are used to provide solutions in everyday technologies, like weather prediction, stock price prediction, voice-to-text conversion, fraud detection, quality assurance, etc. These implementations are only a part of a broad range of applications where these algorithms can offer unique services. In this dissertation, Classical Machine Learning models will be compared with Deep Learning Neural Network models, within the frame of Text Classification. This comparison will be done by using three different feature selection metrics, namely tf.idf, chi square (x2) and devmax.tf. Also, different Neural Network Deep Learning architectures are tested and compared between them, as well as different parameters (input vector size, topology architecture, etc.), which are applied in Neural Networks.
URI: https://uniwacris.uniwa.gr/handle/3000/295
Rights: Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 4.0 Διεθνές
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Type: MSc Thesis
Department: Department of Archival, Library and Information Studies 
School: School of Administrative, Economics and Social Sciences 
Appears in Collections:Master Theses / Μεταπτυχιακές Εργασίες

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