GitHub

Synthetic Minority Oversampling TEchnique (SMOTE)

This custom step helps you generate synthetic data based on an input table, using the Synthetic Minority Oversampling TEchnique (SMOTE). SMOTE is an oversampling technique which identifies new data ...
GitHub

Python_Oversampling_with_SMOTE.py

#It is possible to use Oversampling to increase the significance of the results of the decision tree model at the leaf nodes.
IEEE

A Novel Expandable Borderline Smote Over-Sampling Method for Class Imbalance Problem

Abstract: The class imbalance problem can cause classifiers to be biased toward the majority class and inclined to generate incorrect predictions. While existing studies have proposed numerous ...
EurekAlert!

New AI meA groundbreaking machine learning technique, Counterfactual

A novel approach called Counterfactual Synthetic Minority Oversampling Technique (SMOTE) has been developed to tackle the persistent issue of imbalanced data in healthcare. Traditional models trained ...
IEEE

E-SMOTE: Entropy Based Minority Oversampling for Heart Failure and AIDS Clinical Trails Analysis

Abstract: Machine Learning (ML) algorithms often exhibit reduced performance in the presence of class imbalance, leading to biased results favoring the majority class in a dataset. This imbalance can ...
Scientific Research Publishing

Jason, B. (2021) Smote for Imbalanced Classification with Python.

ABSTRACT: In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps ...