A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
This addresses class imbalance for machine learning practitioners, but it appears incremental as it adapts the existing SMOTE method with quantum processes.
The paper tackled class imbalance in machine learning by proposing Quantum-SMOTE, a method that uses quantum computing techniques like swap tests and quantum rotation to generate synthetic data, and tested it on a Telecom Churn dataset with Random Forest and Logistic Regression, achieving unspecified performance improvements.
The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm's usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of Telecom Churn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.