Research Trends and Applications of Data Augmentation Algorithms

1 code implementation • 18 Jul 2022 • Joao Fonseca, Fernando Bacao

In this paper we identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature.

Data Augmentation

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

2 code implementations • 2 Nov 2017 • Felix Last, Georgios Douzas, Fernando Bacao

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions.

Classification Clustering +2

Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE

1 code implementation • 21 Sep 2017 • Georgios Douzas, Fernando Bacao

While in the basic configuration this region is a hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to a line segment, emulating, in the last case, the SMOTE mechanism.