Mylonas Ioannis (Phd Candidate)

Thesis title: «Data Reduction Techniques for Efficient Instance-based Learning»

Supervisor: Ougiaroglou Stefanos

Advisory Committee Members:

Bratsas Charalampos, Assistant Professor, Dept. of Information and Electronic Engineering, IHU
Kotsakis Rigas, Assistant Professor, Dept. of Information and Electronic Engineering, IHU

Abstract:

The subject of the dissertation is the techniques for reducing training data (Data Reduction Techniques – DRTs) in classification problems. Specifically, these techniques, known as Prototype Selection (PS) algorithms and Prototype Generation algorithms, constitute a preprocessing step for training data with the aim of effective instance-based classification. Generally, these techniques are applied to training data that is organized in rows and columns and are capable of reducing the size of the data while maintaining high classification accuracy and reducing computational cost.

The aim of the dissertation is to develop effective techniques for reducing complex training data, namely data related to data streams [1], distributed data [2],[3], data in non-metric spaces [4], data in complex structures (e.g., graphs), etc. Additionally, the development of data reduction techniques that take into account the phenomenon of concept drift [5] that appears in data streams is also a field of research. This phenomenon occurs when, over time, the distribution of data in space and classes changes.

Βιβλιογραφικές Αναφορές

[1]        ‘Streaming data’, Wikipedia. 17 Αύγουστος 2024. Ημερομηνία πρόσβασης: 9 Οκτώβριος 2024. [Έκδοση σε ψηφιακή μορφή]. Διαθέσιμο στο: https://en.wikipedia.org/w/index.php?title=Streaming_data&oldid=1240848968

[2]        ‘Distributed database’, Wikipedia. 31 Ιούλιος 2024. Ημερομηνία πρόσβασης: 9 Οκτώβριος 2024. [Έκδοση σε ψηφιακή μορφή]. Διαθέσιμο στο: https://en.wikipedia.org/w/index.php?title=Distributed_database&oldid=1237869130

[3]        M. H. ur Rehman, C. S. Liew, A. Abbas, P. P. Jayaraman, T. Y. Wah, και S. U. Khan, ‘Big Data Reduction Methods: A Survey’, Data Sci. Eng., τ. 1, τχ. 4, σελ. 265–284, Δεκεμβρίου 2016, doi: 10.1007/s41019-016-0022-0.

[4]        ‘Hardware and Systems Engineering Design – Linear and Nonlinear Spaces’. Ημερομηνία πρόσβασης: 30 Σεπτέμβριος 2024. [Έκδοση σε ψηφιακή μορφή]. Διαθέσιμο στο: https://www.hwe.design/theories-concepts/foundation-of-the-study-of-linear-algebra-and-functional-analysis/linear-and-nonlinear-spaces

[5]        A. Liu, J. Lu, και G. Zhang, ‘Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations’, IEEE Trans. Fuzzy Syst., τ. 29, τχ. 11, σελ. 3219–3233, Αυγούστου 2021, doi: 10.1109/TFUZZ.2020.3016040.