GitHub

SMOTE-variants for imbalanced learning

The package implements 86 variants of the Synthetic Minority Oversampling Technique (SMOTE). Besides the implementations, an easy to use model selection framework is supplied to enable the rapid ...
GitHub

Oversampling for Imbalanced Learning based on K-Means and SMOTE

K-Means SMOTE is an oversampling method for class-imbalanced data. It aids classification by generating minority class samples in safe and crucial areas of the input space. The method avoids the ...
IEEE

Predicting Stroke Risk Using SMOTE and ROS Machine Learning Techniques

Abstract: The World Stroke Organization has reported that globally 12.2 million people have a stroke for the first time, and 90% of them were related to risk factors that could have been modified.
IEEE

Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Abstract: Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The ...
C&EN

Prediction of 35 Target Per- and Polyfluoroalkyl Substances (PFASs) in California Groundwater Using Multilabel Semisupervised Machine Learning

n = number of observations; %NA = percentage of missing observations. Summary statistics for PFASs (Table 1) and other environmental variables (SI Table S4) were performed to get the number of ...
Frontiers

Machine learning approaches to identify Parkinson's disease using voice signal features

Parkinson's Disease (PD) is the second most common age-related neurological disorder that leads to a range of motor and cognitive symptoms. A PD diagnosis is difficult since its symptoms are quite ...