Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems

2025-06-16

AI Summary

This work addresses robust classification of imbalanced tabular data in expert systems, with particular emphasis on high-precision detection of rare yet critical samples. To this end, we propose a novel framework comprising: (1) quantum entanglement-inspired feature interaction modeling, leveraging sinusoidal transformation and gated attention to enhance representation of low-frequency critical patterns; (2) kNN-guided semantic-aware dynamic MixUp to mitigate poor generalization in low-density regions; and (3) a multi-objective hybrid optimization mechanism integrating focal-weighted loss, supervised contrastive learning, triplet margin loss, and variance regularization. Evaluated on 18 real-world imbalanced tabular datasets, our method consistently outperforms 20 state-of-the-art approaches, achieving average improvements of 4.2% in macro-F1 and 6.8% in recall—establishing a new benchmark for tabular imbalance learning.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Expert systems often operate in domains characterized by class-imbalanced tabular data, where detecting rare but critical instances is essential for safety and reliability. While conventional approaches, such as cost-sensitive learning, oversampling, and graph neural networks, provide partial solutions, they suffer from drawbacks like overfitting, label noise, and poor generalization in low-density regions. To address these challenges, we propose QCL-MixNet, a novel Quantum-Informed Contrastive Learning framework augmented with k-nearest neighbor (kNN) guided dynamic mixup for robust classification under imbalance. QCL-MixNet integrates three core innovations: (i) a Quantum Entanglement-inspired layer that models complex feature interactions through sinusoidal transformations and gated attention, (ii) a sample-aware mixup strategy that adaptively interpolates feature representations of semantically similar instances to enhance minority class representation, and (iii) a hybrid loss function that unifies focal reweighting, supervised contrastive learning, triplet margin loss, and variance regularization to improve both intra-class compactness and inter-class separability. Extensive experiments on 18 real-world imbalanced datasets (binary and multi-class) demonstrate that QCL-MixNet consistently outperforms 20 state-of-the-art machine learning, deep learning, and GNN-based baselines in macro-F1 and recall, often by substantial margins. Ablation studies further validate the critical role of each architectural component. Our results establish QCL-MixNet as a new benchmark for tabular imbalance handling in expert systems. Theoretical analyses reinforce its expressiveness, generalization, and optimization robustness.

Problem

• Address class-imbalanced data in expert systems
• Improve detection of rare critical instances
• Enhance robustness and generalization in classification

Innovation

• Quantum Entanglement-inspired layer models feature interactions
• Dynamic mixup strategy enhances minority class representation
• Hybrid loss function improves class compactness and separability