A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)

arXiv:2402.17398v1 Announce Type: cross
Abstract: 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 …

abstract arxiv class computing cs.ai cs.lg data datasets machine machine learning novel oversampling paper processes quant-ph quantum quantum computing smote solution solve synthetic synthetic data tests type