MATLAB-Source-Code-Oversampling-Methods
Oversampling methods
This repository provides implementations of four oversampling methods to address class imbalance in binary data classification
This repository contains the source code for four oversampling methods that I wrote in MATLAB: 1) SMOTE 2) Borderline SMOTE 3) Safe Level SMOTE 4) ASUWO (Adaptive Semi-Unsupervised Weighted Oversampling)
37 stars
2 watching
17 forks
Language: Matlab
last commit: over 8 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| Software implementing a blind pixel-level non-local method for image denoising using additive Gaussian white noise. | 49 |
| An image denoising algorithm implementing a statistical approach to improve traditional methods | 42 |
| A collection of unconstrained optimization algorithms implemented in MATLAB | 67 |
| A Matlab implementation of an image denoising scheme based on sparse coding and trilateral weighting. | 91 |
| MATLAB implementation of mathematical modeling algorithms and applications | 37 |
| Converting dirty machine learning code into clean, modular, and reusable components using the Pipe and Filter Design Pattern for Machine Learning. | 18 |
| Source code accompanying a textbook on machine learning in MATLAB | 84 |
| Provides lecture notes and MATLAB code for data science methods | 49 |
| Provides MATLAB implementation of reverse filtering methods for deblurring noisy images | 3 |
| A collection of stochastic optimization algorithms for large-scale machine learning problems | 221 |
| Software package for stain separation and color normalization in histopathological images using computational pathology techniques | 66 |
| Provides PyTorch implementation of a method to address noisy labels in medical image segmentation. | 71 |
| A collection of Matlab scripts and resources for learning digital image processing concepts | 43 |
| Provides a Matlab interface to SNOPT for nonlinear optimization | 56 |
| The codebase provides MATLAB implementations of machine learning concepts from S. Theodoridis' book | 66 |