Results 1 to 10 of about 13,919 (263)
The European Zoological Journal, 2023 The Bray–Curtis dissimilarity is widely used to calculate β diversity on abundance data. However, the effect of undersampling on this index has received limited attention and only few studies addressed this topic. The paper aimed to investigate the error
Sönke Hardersen, Gianandrea La Porta
exaly +3 more sources
Information (Switzerland), 2023 Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models.
Okan Bulut +2 more
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Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
Applied Sciences (Switzerland), 2023 In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and misclassifying phishing emails.
Yijia Xu, Qinglin Qi
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Improving Software Defect Prediction in Noisy Imbalanced Datasets
Applied Sciences, 2023 Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise.
Haoxiang Shi +3 more
doaj +1 more source
ComTech, 2020 The research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class.
Masithoh Yessi Rochayani +2 more
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Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
Future Internet, 2023 This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling before
Sikha Bagui +4 more
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Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models
ISPRS International Journal of Geo-Information, 2023 Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research.
Yingze Song +7 more
doaj +1 more source
Applied Sciences, 2021 In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class.
Mohamed S. Kraiem +2 more
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Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion
Remote Sensing, 2018 Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture.
Yilong Zhang +4 more
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PSU: Particle Stacking Undersampling Method for Highly Imbalanced Big Data
IEEE Access, 2020 Imbalanced classes are a common problem in machine learning, and the computational costs required for proper resampling increases with the data size. In this study, a simple and effective undersampling method, named particle stacking undersampling (PSU ...
Yong-Seok Jeon, Dong-Joon Lim
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