Brief

The imbalanced-learn Python library offers a comprehensive solution for addressing class imbalance in machine learning datasets. It consolidates various resampling techniques, such as SMOTE and under-sampling methods, into a single, scikit-learn-compatible package. This library simplifies the process of building robust machine learning pipelines by ensuring that resampling is applied correctly during cross-validation, preventing data leakage and improving model performance on imbalanced data. AI

Researchers have developed Q-SYNTH, a novel hybrid quantum-classical framework designed to address the challenge of imbalanced data in credit card fraud detection. This system uses a parameterized quantum circuit as the generator and a classical neural network as the discriminator to synthesize minority-class fraud samples. Evaluations show Q-SYNTH offers a promising balance between statistical fidelity to real fraud data and improved downstream fraud detection performance, outperforming some classical baselines in specific metrics. AI