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Akademický článek

A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems

Autor: Yuxuan Yang, Hadi Akbarzadeh Khorshidi, Uwe Aickelin

Publikováno v: Frontiers in Digital Health, Vol 6 (2024)

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods

Externí odkaz: https://doaj.org/article/e5a2e31b16c04ad7bec05f7e9dcfa281

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Akademický článek

Comparing Different Oversampling Methods in Predicting Multi-Class Educational Datasets Using Machine Learning Techniques

Autor: Tariq Muhammad Arham, Sargano Allah Bux, Iftikhar Muhammad Aksam, Habib Zulfiqar

Publikováno v: Cybernetics and Information Technologies, Vol 23, Iss 4, Pp 199-212 (2023)

Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance

Externí odkaz: https://doaj.org/article/d10c4116149e48c59a27f351e7cc3a74

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Akademický článek

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Akademický článek

Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

Autor: Jie Hao, Changsheng Zhu, Xiuting Guo

Publikováno v: IEEE Access, Vol 9, Pp 6478-6487 (2021)

Under the background of big data, the use of massive online data to improve the real-time characteristics and reliability of wind power prediction and to reduce the impact of wind farms on the power grid makes the power supply and demand balance impo

Externí odkaz: https://doaj.org/article/9aa88ca80e4e41c5af2f4df0a57ea8bc

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Akademický článek

Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets

Autor: Yen-Ru Chen, Jenq-Shiou Leu, Sheng-An Huang, Jui-Tang Wang, Jun-Ichi Takada

Publikováno v: IEEE Access, Vol 9, Pp 73103-73109 (2021)

In the past few years, Peer-to-Peer lending (P2P lending) has grown rapidly in the world. The main idea of P2P lending is disintermediation and removing the intermediaries like banks. For a small business and some individuals without enough credit or

Externí odkaz: https://doaj.org/article/a56925a0b965464c9fd9d702681de758

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Akademický článek

Comparison of three classes of Marginal Risk Set Model in predicting infant mortality among newborn babies at Kigali University Teaching Hospital, Rwanda, 2016

Autor: Paul Gatabazi, Sileshi Fanta Melesse, Shaun Ramroop

Publikováno v: BMC Pediatrics, Vol 20, Iss 1, Pp 1-11 (2020)

Abstract Background The Infant Mortality Rate (IMR) in Sub-Saharan Africa (SSA) remains the highest relatively to the rest of the world. In the past decade, the policy on reducing infant mortality in SSA was reinforced and both infant mortality and p

Externí odkaz: https://doaj.org/article/047e2d33949e4e4d8746030e19636a5d

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Akademický článek

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Akademický článek

Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma

Autor: Jieqin Lv, Xiaohui Chen, Xinran Liu, Dongyang Du, Wenbing Lv, Lijun Lu, Hubing Wu

Publikováno v: Frontiers in Oncology, Vol 12 (2022)

ObjectivesTo develop and validate the imbalanced data correction based PET/CT radiomics model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma (LUAD).MethodsA total of 183 patients (148/35 non-metastasis/LNM) with p

Externí odkaz: https://doaj.org/article/896409e55562406daf04346f06108595

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Akademický článek

Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy

Autor: Giulia Varotto, Gianluca Susi, Laura Tassi, Francesca Gozzo, Silvana Franceschetti, Ferruccio Panzica

Publikováno v: Frontiers in Neuroinformatics, Vol 15 (2021)

Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resamp

Externí odkaz: https://doaj.org/article/1b9ae50c9eed4277bc3395713a3db303

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