Results 71 to 80 of about 219,349 (181)
PhysioDimClassifier—imbalance data classifier model for IoMT-based remote patient monitoring systems
MethodsXRemote patient monitoring systems (RPMS) using the Internet of Medical Things (IoMT) continuously collect and exchange periodic sensor-observations through communication modules.
Sayyed Johar, G.R. Manjula
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A Recapitulation of Imbalanced Data
International Journal of Innovative Technology and Exploring Engineering, 2020 In today’s authentic universe almost all applications are imbalanced. Data imbalance is growing faster than ever before as many systems are interested in extracting knowledge from lake of data. Imbalance issue occurs because required data is very rare and using that rare data if classification is done we may lead to inaccurate result.
Shaheen Layaq*, Dr. B. Manjula
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Classification performance assessment for imbalanced multiclass data
Scientific ReportsThe evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality solutions, especially given the pronounced context-sensitivity of certain systems, particularly in fields such as biomedicine.
Jesús S. Aguilar-Ruiz, Marcin Michalak
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Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams
IEEE Access, 2019 Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI).
Hang Zhang +4 more
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Shape Penalized Decision Forests for Imbalanced Data Classification
IEEE AccessClass imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set.
Rahul Goswami +4 more
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Comparison of Sampling Techniques for Imbalanced Data Classification
วารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์, 2018 Imbalanced data is a problem in the machine learning process for data classification, which results in low classification efficiency. It has also been found that random sampling techniques are used in several ways for solving low performance problems due
Karn Nasritha +2 more
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Applied SciencesClassifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains.
Ruiao Zou, Nan Wang
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An empirical evaluation of imbalanced data strategies from a practitioner's point of view
, 2018 This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets (sampled to imbalance rates of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline (just the base ...
Franceschinell, Rodrigo A. +1 more
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BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA
Malaysian Journal of Computing, 2014 Many real-world data sets exhibit imbalanced class distributions in which almost all instances are assigned to one class and far fewer instances to a smaller, yet usually interesting class. Building classification models from such imbalanced data sets is
Terence Yong Koon Beh +2 more
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Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.
, 2020 Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions focus mainly on the class distribution problem. However,
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