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OASIS-SB: a sex-balanced, distribution-preserving, synthetic phenotypic dataset for bias-resilient clinical prediction. [PDF]
Front Comput NeurosciDhariwal N.
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An enhanced diabetic retinopathy detection approach using optimized deep learning technique. [PDF]
Sci RepDarwish SM, Milad KG, Ibrahim REE.
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Air quality index AQI classification based on hybrid particle swarm and grey wolf optimization with ensemble machine learning model. [PDF]
Sci RepElabd E +4 more
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Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach. [PDF]
JMIR Pediatr ParentAlSaad R +5 more
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International Journal of Information Retrieval Research, 2021
The tremendous amount of data generated through IoT can be imbalanced causing class imbalance problem (CIP). CIP is one of the major issues in machine learning where most of the samples belong to one of the classes, thus producing biased classifiers. The authors in this paper are working on four imbalanced datasets belonging to diverse domains.
Ankita Bansal +3 more
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The tremendous amount of data generated through IoT can be imbalanced causing class imbalance problem (CIP). CIP is one of the major issues in machine learning where most of the samples belong to one of the classes, thus producing biased classifiers. The authors in this paper are working on four imbalanced datasets belonging to diverse domains.
Ankita Bansal +3 more
openaire +1 more source
SMOTE-Text: A Modified SMOTE for Turkish Text Classification
2021One of the most common problems faced by large enterprise companies is the loss of knowhow after employee’s job replacements and quits. Creating a well-organized, indexed, connected, user friendly and sustainable digital enterprise memory can solve this problem and creates a practical knowhow transfer to new recruited personnel.
Nur Curukoglu, Alper Ozpinar
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Proceedings of the 2017 International Conference on Information Technology, 2017
In recent years, classification of imbalanced data has troubled most classification models because of the imbalanced class distribution. Synthetic Minority Oversampling Technique (SMOTE) is one of the solutions at data level, but this kind of method doesn't consider the distribution of the data set, thus the result is not satisfied.
Cheng Zhang +3 more
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In recent years, classification of imbalanced data has troubled most classification models because of the imbalanced class distribution. Synthetic Minority Oversampling Technique (SMOTE) is one of the solutions at data level, but this kind of method doesn't consider the distribution of the data set, thus the result is not satisfied.
Cheng Zhang +3 more
openaire +1 more source
SMOTE Inspired Extension for Differential Evolution
2022Although differential evolution (DE) is a well established optimisation method, proven on a wide variety of problems, modifications are proposed on a regular basis attempting to ever more improve its performance. Typical avenues for improvement include the introduction of new (mutation) operators or parameter control schemes.
Bajer, Dražen +2 more
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