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https://ruomo.lib.uom.gr/handle/7000/1572
Title: | Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining |
Authors: | Tsagalidis, Evangelos Evangelidis, Georgios |
Type: | Article |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | meteorological data mining and machine learning class imbalance classification randomized undersampling SMOTE oversampling undersampling using temporal distances |
Issue Date: | 4-Dec-2022 |
Publisher: | Multidisciplinary Digital Publishing Institute |
Source: | Applied Sciences |
Volume: | 12 |
Issue: | 23 |
First Page: | 12402 |
Abstract: | 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 |
Other Identifiers: | 10.3390/app122312402 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
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2022_applsci-12-12402-v3.pdf | 308,43 kB | Adobe PDF | View/Open |
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