Distribution Learning Based on Evolutionary Algorithm-Assisted Deep Neural Networks for Imbalanced Image Classification
提出MEDA_LUDE算法，用进化算法优化深度神经网络的潜在特征分布，在不平衡图像分类中提升少数类样本的质量和多样性，在MNIST、CIFAR-10和工业织物缺陷数据集上均取得显著精度提升。
Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution algorithm-based latent feature distribution evolution (MEDA_LUDE) algorithm, an evolutionary algorithm-assisted deep distribution learning framework that optimizes latent feature distributions through a multivariate Gaussian mixture (GM) assumption and a novel four-phase training strategy. We introduce a large-margin GM (L-GM) loss to dynamically model covariances for feature learning and design a MEDA that evolves latent features via a similarity-guided fitness function, thus enhancing diversity while preserving synthesis quality. Extensive experiments demonstrate significant improvements: MEDA_LUDE achieves 95.9% accuracy on MNIST (imbalanced ratio-IR:100), surpassing state-of-the-art methods by 1.26% on CIFAR-10. For industrial fabric defect data sets, it elevates accuracy by 1.45% on DHU-FD and 0.92% on ALIYUN-FD, especially with precision and G-mean improvements of 2.5% and 1.17%, respectively, on DHU-FD. Visualizations confirm that MEDA_LUDE generates minority samples with superior quality-diversity tradeoffs. The framework's success in real-world fabric defect classification underscores its practical value in addressing imbalanced learning challenges.