Oversampling for Imbalanced Learning Based on K-Means and SMOTE

By Felix Last, Georgios Douzas, Fernando Bacao. Information Sciences, 2017

940 citations recorded by the source corpus.

Abstract 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. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE (synthetic minority oversampling technique), which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 90 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation 1 is made available in the Python programming language.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e7f3798d3bc0be85baf96bd36496f712bd719678

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Rohitash Chandra
2. Azal Ahmad Khan
3. Tarid Wongvorachan
4. Surina He
5. Omkar Chaudhari
6. Okan Bulut

In Electrical and Electronic Engineering

1. Rohitash Chandra
2. Azal Ahmad Khan
3. Tarid Wongvorachan
4. Surina He
5. Omkar Chaudhari
6. Okan Bulut

Citations in the corpus

• A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, background): “For the extremely imbalanced case, ROS presented an overfitting issue, which might be due to the repeated replication of the minority class [47–49].”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “K-means SMOTE [216] consists of three steps: clustering, filtering, and oversampling.”