Oversampling for Imbalanced Learning Based on K-Means and SMOTE

TL;DR

This paper introduces a simple, noise-reducing oversampling technique combining k-means clustering with SMOTE to improve classification on imbalanced datasets, outperforming existing methods.

Contribution

The paper proposes a novel oversampling method that integrates k-means clustering with SMOTE, reducing noise and enhancing performance on imbalanced data.

Findings

Outperforms other oversampling methods on 71 datasets

Effectively reduces noise in synthetic data generation

Improves classification accuracy on imbalanced datasets

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 oversampling, which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 71 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 is made available in the python programming language.