LAUSR

Synthetic minority oversampling for function approximation problems

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0 Published in 2019 at "International Journal of Intelligent Systems"

DOI: 10.1002/int.22120

Abstract: Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). However, imbalanced data… read more here.

Keywords: function approximation; approximation; approximation problems; minority oversampling ... See more keywords

Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm

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0 Published in 2017 at "Multimedia Tools and Applications"

DOI: 10.1007/s11042-017-5023-0

Abstract: Pathological brain detection is an automated computer-aided diagnosis for brain images. This study provides a novel method to achieve this goal.We first used synthetic minority oversampling to balance the dataset. Then, our system was based… read more here.

Keywords: pathological brain; brain detection; synthetic minority; jaya algorithm ... See more keywords

Synthetic minority oversampling of vital statistics data with generative adversarial networks

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0 Published in 2020 at "Journal of the American Medical Informatics Association : JAMIA"

DOI: 10.1093/jamia/ocaa127

Abstract: Abstract Objective Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely… read more here.

Keywords: statistics data; generative adversarial; minority oversampling; synthetic minority ... See more keywords

A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification.

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1 Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3197156

Abstract: Data imbalance is a common phenomenon in machine learning. In the imbalanced data classification, minority samples are far less than majority samples, which makes it difficult for minority to be effectively learned by classifiers A… read more here.

Keywords: synthetic minority; minority; imbalanced data; oversampling technique ... See more keywords

A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI

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1 Published in 2022 at "Computational Intelligence and Neuroscience"

DOI: 10.1155/2022/9283293

Abstract: During the last few decades, the quality of water has deteriorated significantly due to pollution and many other issues. As a consequence of this, there is a need for a model that can make accurate… read more here.

Keywords: water; machine learning; synthetic minority; model ... See more keywords