Quantum SMOTE with Angular Outliers: Redefining Minority Class Handling

2025-01-31

AI Summary

To address class imbalance in machine learning—particularly the sparsity of minority-class samples and bias induced by angular outliers (AOLs)—this paper proposes Quantum-SMOTEV2, a clustering-free minority-class oversampling method grounded in single-center quantum rotation and quantum swap testing. Innovatively integrating angular distance metrics and an AOL-guided sampling mechanism, it establishes a low-depth, tunable classical-quantum hybrid framework. Evaluated on the Cell-to-Cell Telecom dataset, Quantum-SMOTEV2 achieves significant improvements in accuracy, F1-score, AUC-ROC, and AUC-PR for RF, KNN, and neural network classifiers using only 30–36% synthetic samples—outperforming conventional methods requiring 50% synthesis. The approach demonstrates superior efficiency, robustness against outliers, and scalability to larger datasets and diverse classifiers.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Imbalanced Data
• Minority Class
• Machine Learning Performance

Innovation

• Quantum-SMOTEV2
• Quantum Computing
• Data Imbalance