A Quantum Neural Network-Based Learning System for Imbalanced Data Classification
Keywords:
Hybrid models, Imbalanced data, Minority class detection, Quantum machine learning, Quantum neural networks, Variational circuitsAbstract
One of the biggest problems is imbalanced data classification in machine learning. The minority class is usually represented less often, causing biased predictions and a lack of generalizability. The traditional machine learning models and deep learning models maximize the overall accuracy, which results in the accuracy paradox, and the minority class detection is weak in applications like fraud detection, medical diagnosis, or anomaly detection. In order to overcome the above drawbacks, the hybrid Quantum Neural Network (QNN) learning system for imbalanced data is suggested in this paper. The proposed framework is a hybrid between classical data preprocessing and a variational quantum circuit architecture, in which the input features are encoded into qubits via angle encoding, and then processed within a layer of parameterized quantum circuits, and the output features are mapped by measurement. To enhance minority class learning in optimization, a weighted cross-entropy loss function is used. The model is tested on an imbalanced dataset and compared with several basic models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, XGBoost, and LightGBM. The experimental results show the improvements in minority class recall and F1-scores, achieving an overall accuracy of 94.3%, and a 7%-10% higher F1-score than baseline approaches. The results suggest that QNN-based architectures may be a suitable solution to improve the classification performance in imbalanced learning environments.
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