An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets

By György Kovács. Applied Soft Computing, 2019

299 citations recorded by the source corpus.

Abstract Learning and mining from imbalanced datasets gained increased interest in recent years. One simple but efficient way to increase the performance of standard machine learning techniques on imbalanced datasets is the synthetic generation of minority samples. In this paper, a detailed, empirical comparison of 85 variants of minority oversampling techniques is presented and discussed involving 104 imbalanced datasets for evaluation. The goal of the work is to set a new baseline in the field, determine the oversampling principles leading to the best results under general circumstances, and also give guidance to practitioners on which techniques to use with certain types of datasets.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1a10ca400d5d6aaa60ecb9effb6171876a4832a6

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In Artificial Intelligence

1. Tarid Wongvorachan
2. Surina He
3. Okan Bulut

In Electrical and Electronic Engineering

1. Tarid Wongvorachan
2. Surina He
3. Okan Bulut

Citations in the corpus

• A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, methodology): “…techniques such as ENN [24] were also found to work well for datasets with a low to moderate imbalance ratio, while the other SMOTE variants such as polynomial SMOTE [25] could handle both datasets with a moderately imbalanced class and an extremely imbalanced class equally well [11,26].”