Quantum SMOTE with Angular Outliers: Redefining Minority Class Handling

TL;DR

Quantum-SMOTEV2 employs quantum computing techniques to improve minority class data synthesis, significantly enhancing machine learning model performance with fewer synthetic samples and scalable quantum circuits.

Contribution

It introduces Quantum-SMOTEV2, a novel quantum-based oversampling method that focuses on angular outliers, offering improved performance and scalability over previous quantum SMOTE variants.

Findings

Enhanced model metrics at 30-36% SMOTE levels

Maintains key features for dataset adaptation

Scalable quantum circuits for high-dimensional data

Abstract

This paper introduces Quantum-SMOTEV2, an advanced variant of the Quantum-SMOTE method, leveraging quantum computing to address class imbalance in machine learning datasets without K-Means clustering. Quantum-SMOTEV2 synthesizes data samples using swap tests and quantum rotation centered around a single data centroid, concentrating on the angular distribution of minority data points and the concept of angular outliers (AOL). Experimental results show significant enhancements in model performance metrics at moderate SMOTE levels (30-36%), which previously required up to 50% with the original method. Quantum-SMOTEV2 maintains essential features of its predecessor (arXiv:2402.17398), such as rotation angle, minority percentage, and splitting factor, allowing for tailored adaptation to specific dataset needs. The method is scalable, utilizing compact swap tests and low depth quantum circuits to accommodate a large number of features. Evaluation on the public Cell-to-Cell Telecom dataset with Random Forest (RF), K-Nearest Neighbours (KNN) Classifier, and Neural Network (NN) illustrates that integrating Angular Outliers modestly boosts classification metrics like accuracy, F1 Score, AUC-ROC, and AUC-PR across different proportions of synthetic data, highlighting the effectiveness of Quantum-SMOTEV2 in enhancing model performance for edge cases.