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https://ruomo.lib.uom.gr/handle/7000/1572
Τίτλος: | Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining |
Συγγραφείς: | Tsagalidis, Evangelos Evangelidis, Georgios |
Τύπος: | Article |
Θέματα: | FRASCATI::Natural sciences::Computer and information sciences |
Λέξεις-Κλειδιά: | meteorological data mining and machine learning class imbalance classification randomized undersampling SMOTE oversampling undersampling using temporal distances |
Ημερομηνία Έκδοσης: | 4-Δεκ-2022 |
Εκδότης: | Multidisciplinary Digital Publishing Institute |
Πηγή: | Applied Sciences |
Τόμος: | 12 |
Τεύχος: | 23 |
Πρώτη Σελίδα: | 12402 |
Επιτομή: | We deal with the problem of class imbalance in data mining and machine learning classification algorithms. This is the case where some of the class labels are represented by a small number of examples in the training dataset compared to the rest of the class labels. Usually, those minority class labels are the most important ones, implying that classifiers should primarily perform well on predicting those labels. This is a well-studied problem and various strategies that use sampling methods are used to balance the representation of the labels in the training dataset and improve classifier performance. We explore whether expert knowledge in the field of Meteorology can enhance the quality of the training dataset when treated by pre-processing sampling strategies. We propose four new sampling strategies based on our expertise on the data domain and we compare their effectiveness against the established sampling strategies used in the literature. It turns out that our sampling strategies, which take advantage of expert knowledge from the data domain, achieve class balancing that improves the performance of most classifiers. |
URI: | https://doi.org/10.3390/app122312402 https://ruomo.lib.uom.gr/handle/7000/1572 |
ISSN: | 2076-3417 |
Αλλοι Προσδιοριστές: | 10.3390/app122312402 |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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2022_applsci-12-12402-v3.pdf | 308,43 kB | Adobe PDF | Προβολή/Ανοιγμα |
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