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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
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.
ISSN: 2076-3417
Other Identifiers: 10.3390/app122312402
Appears in Collections:Department of Applied Informatics

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