A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
CoRR(2024)
摘要
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. The
approach is tested on a public dataset of TelecomChurn and evaluated alongside
two prominent classification algorithms, Random Forest and Logistic Regression,
to determine its impact along with varying proportions of synthetic data.
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