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ClusterOSS: a new undersampling method for imbalanced learning
(Universidade de São Paulo - USPUniversidade Federal de São Carlos - UFSCarCentro de Robótica de São Carlos - CROBSociedade Brasileira de Computação - SBCSociedade Brasileira de Automática – SBASão Carlos, 2014-10)
A dataset is said to be imbalanced when its classes are disproportionately represented in terms of the number of instances they contain. This problem is common in applications such as medical diagnosis of rare diseases, ...
Handling imbalanced datasets through Optimum-Path Forest
(2022-04-22)
In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Such an advance is partially due to the ...
Data analysis in python: Anonymized features and imbalanced data target
(2017-04-25)
Remaining useful life (RUL) of an equipment or system is a prognostic value that depends on data gathered from multiple and diverse sources. Moreover, assumed for the sake of the present study as a binary classification ...
Symbolic one-class learning from imbalanced datasets: Application in medical diagnosis
(World Scientic Publishing Company, 2009)
Symbolic one-class learning from imbalanced datasets: Application in medical diagnosis
(World Scientic Publishing Company, 2009)
Balanced training of a hybrid ensemble method for imbalanced datasets: a case of emergency department readmission prediction
(Springer, 2020)
Dealing with imbalanced datasets is a recurrent issue in health-care data processing. Most literature deals with small academic datasets, so that results often do not extrapolate to the large real-life datasets, or have ...
Emergency department readmission risk prediction: A case study in Chile
(Springer Verlag, 2017)
Short time readmission prediction in Emergency Depart-ments (ED) is a valuable tool to improve both the ED managementand the healthcare quality. It helps identifying patients requiring fur-ther post-discharge attention ...